Amazon Alexa Shopping in London/Cambridge is hiring Applied Scientists and Applied Science Interns!
You: Alexa, I am looking for a new career opportunity! I want to do exciting and innovative work in AI and impact millions of people. What do you suggest?
Alexa: The Alexa Shopping team is looking for Applied Scientists to help me become the best personal shopping assistant. Do you want to hear more?
You: Yes, please!
The Alexa Shopping team in the UK is looking for both applied scientists and applied science interns (for summer 2020) investigating and developing machine learning and privacy enhancing technologies to help Alexa turn into the best and most trustworthy personal shopping assistant.
The ideal candidate has expertise in one or several of the following fields: Machine Learning, Applied/Theoretical Statistics, Crowdsourcing, Differential Privacy, Algorithmic Fairness. S/he also shows good intuition with the technologies behind personal assistants and the needs of customers. The ability to write clearly and speak convincingly as evidenced through participation in academic conferences and service in the scientific community are a must.
Applied Scientist: https://www.amazon.jobs/en/jobs/979175/applied-scientist
Applied Science Internships: https://www.amazon.jobs/en/jobs/1003772/2020-machine-learning-internship-alexa-shopping
Amazon is an Equal Opportunity-Affirmative Action Employer – Minority/Female/Disability/Veteran/Gender Identity/Sexual Orientation
For more information about the position, please contact Tom Diethe (tdiethe@amazon.com) or Andreas Damianou (damianou@amazon.com)
✔️ @ApplyTime
You: Alexa, I am looking for a new career opportunity! I want to do exciting and innovative work in AI and impact millions of people. What do you suggest?
Alexa: The Alexa Shopping team is looking for Applied Scientists to help me become the best personal shopping assistant. Do you want to hear more?
You: Yes, please!
The Alexa Shopping team in the UK is looking for both applied scientists and applied science interns (for summer 2020) investigating and developing machine learning and privacy enhancing technologies to help Alexa turn into the best and most trustworthy personal shopping assistant.
The ideal candidate has expertise in one or several of the following fields: Machine Learning, Applied/Theoretical Statistics, Crowdsourcing, Differential Privacy, Algorithmic Fairness. S/he also shows good intuition with the technologies behind personal assistants and the needs of customers. The ability to write clearly and speak convincingly as evidenced through participation in academic conferences and service in the scientific community are a must.
Applied Scientist: https://www.amazon.jobs/en/jobs/979175/applied-scientist
Applied Science Internships: https://www.amazon.jobs/en/jobs/1003772/2020-machine-learning-internship-alexa-shopping
Amazon is an Equal Opportunity-Affirmative Action Employer – Minority/Female/Disability/Veteran/Gender Identity/Sexual Orientation
For more information about the position, please contact Tom Diethe (tdiethe@amazon.com) or Andreas Damianou (damianou@amazon.com)
✔️ @ApplyTime
amazon.jobs
Applied Scientist
You: Alexa, I am looking for a new impactful role where I could tackle the next big research challenge in search, what do you suggest?Alexa: The Alexa Shopping team is looking for applied research leaders to help me become the best personal shopping assistant.…
Two Studentships in Gerontology - DEADLINE 23 January
A fully funded studentship awarded by the Economic and Social Research Council (ESRC) South Coast Doctoral Training Partnership (SCDTP) commencing in 2020/21 Academic Year.
Supervisory Team
Prof. Athina Vlachantoni and Prof. Maria Evandrou (both in Department of Gerontology, Centre for Population Change and Centre for Research on Ageing, University of Southampton)
Older persons from minority ethnic communities in the UK have been shown to be at a financial disadvantage compared to the White British population, however there remain questions about the role of pensions in ethnic elders’ income and the ways in which they prepare financially for later life. The project will combine analysis of the UK Household Longitudinal Survey with semi-structured interviews with older individuals from Indian, Pakistani and Bangladeshi communities in the UK, in order to explore the composition of ethnic elders’ income, the role of pensions in such income, and alternative (non-pension) strategies for financial adequacy in later life.
Skills Required
Essential: Background in a social sciences subject, ideally linked to ethnicity and/ or ageing over the life course; Knowledge of logistic regression modelling; Knowledge of qualitative interviewing. Desirable: Knowledge of mixed methods research; experience in using USoc or other large-scale datasets.
More info
✔️ @ApplyTime
A fully funded studentship awarded by the Economic and Social Research Council (ESRC) South Coast Doctoral Training Partnership (SCDTP) commencing in 2020/21 Academic Year.
Supervisory Team
Prof. Athina Vlachantoni and Prof. Maria Evandrou (both in Department of Gerontology, Centre for Population Change and Centre for Research on Ageing, University of Southampton)
Older persons from minority ethnic communities in the UK have been shown to be at a financial disadvantage compared to the White British population, however there remain questions about the role of pensions in ethnic elders’ income and the ways in which they prepare financially for later life. The project will combine analysis of the UK Household Longitudinal Survey with semi-structured interviews with older individuals from Indian, Pakistani and Bangladeshi communities in the UK, in order to explore the composition of ethnic elders’ income, the role of pensions in such income, and alternative (non-pension) strategies for financial adequacy in later life.
Skills Required
Essential: Background in a social sciences subject, ideally linked to ethnicity and/ or ageing over the life course; Knowledge of logistic regression modelling; Knowledge of qualitative interviewing. Desirable: Knowledge of mixed methods research; experience in using USoc or other large-scale datasets.
More info
✔️ @ApplyTime
South Coast Doctoral Training Partnership
Apply - South Coast Doctoral Training Partnership
Our call for applications for studentships commencing in 2026/27 is now open (from 26th January 2025, 12:00am) The deadline for application is 16:00 GMT 16th Jan 2026. Below are our current application procedures Application Procedure The application…
Apply Time Positions pinned «#ApplyTime از زمانی که کانال #اپلای تایم راهاندازی شده است، اندکی بیش از 3 سال میگذرد! شاید باور کردنش دشوار باشد، ولی در طول این 3 سال بیش از 5000 موقعیت و یا بورسیهی تحصیلی در مقاطع گوناگون، رشتههای متنوع، و کشورهای مختلف با شما به اشتراک گذارده شده…»
Dr. Mahdi Imani, an assistant professor at the Department of Electrical and Computer Engineering at the George Washington University, seeks for multiple PhD students with interests in Machine Learning, Reinforcement Learning and Statistics. Ideal candidates may have:
- Master’s degree in electrical/computer engineering or computer science.
- Strong background in mathematics and statistics.
- Good programming skills (e.g., Python).
Prospective students may email their CV, trannoscripts and English test scores at imani.gwu@gmail.com. For more information, see https://web.seas.gwu.edu/imani/.
Self-supported postdoctoral and visiting scholars are encouraged to contact as well.
--
Mahdi Imani, Ph.D.
Assistant Professor
Dept. of Electrical and Computer Eng.
George Washington University
https://web.seas.gwu.edu/imani/
✔️ @ApplyTime
- Master’s degree in electrical/computer engineering or computer science.
- Strong background in mathematics and statistics.
- Good programming skills (e.g., Python).
Prospective students may email their CV, trannoscripts and English test scores at imani.gwu@gmail.com. For more information, see https://web.seas.gwu.edu/imani/.
Self-supported postdoctoral and visiting scholars are encouraged to contact as well.
--
Mahdi Imani, Ph.D.
Assistant Professor
Dept. of Electrical and Computer Eng.
George Washington University
https://web.seas.gwu.edu/imani/
✔️ @ApplyTime
PostDoc Researcher at Accenture Labs Dublin - Graph Representation Learning and Explainable AI
Accenture Labs Dublin is looking for a Post-Doctoral researcher in the domain of Graph Representation Learning and Explainable AI.
You will join a newly-created, multi-partner project whose goal is to identify factors that can cause development of new medical conditions, and worsen the quality of life of cancer survivors. The project will analyse patient’s clinical, genomic, behavioural data and existing open data in order to determine a follow-up adapted to the individual needs. The length of the PostDoc is 3 years.
You will join our team of AI researchers and engineers, and work on research activities focused on explainable AI and machine learning on knowledge graphs.
You will be in charge of designing, developing, evaluating, and applying novel models. That will include software development (including contributing to our open source stack [1]), carrying out experiments, and publishing results in academic venues.
More precisely, you will design interpretable machine learning models to infer knowledge from a graph of clinical, genomic, and behavioural data. Explanations will use a wide range of techniques, such as rules derived from the deep learning models, interpretable machine learning, or graph-based explanations based on network analysis.
Requirements
* PhD in computer science, statistics, mathematics or related field.
* Proven communication skills (talks, presentations, academic publications)
* Strong foundation in mathematics, statistics and probability
* Strong knowledge of Machine Learning foundations
* Knowledge of mainstream Deep Learning architectures
* Ability to work creatively and analytically in a problem-solving environment
* Strong Python programming skills
* Hands-on experience with machine learning frameworks (e.g. TensorFlow, PyTorch), and scientific Python (e.g. numpy)
* Eagerness to contribute in a team-oriented environment
Preferred Qualifications
* Experience with graph-based knowledge representation (e.g. knowledge graphs)
* Familiarity with graph representation learning (e.g. knowledge graph embeddings, graph neural networks)
* Experience with explainable AI or interpretable machine learning techniques
* Working knowledge of Linux OS and shell noscripting
* Hands-on experience with git and issue tracking systems
About Accenture Labs Dublin
Accenture Labs is a network of R&D labs distributed on seven locations worldwide, home of over 200 applied R&D experts. The Dublin Labs team focuses on artificial intelligence, with a strong emphasis on explainable AI, machine learning on knowledge graphs (graph representation learning), and computational creativity. The lab is co-located with the over 100 designers, developers and domain experts at The Dock, Accenture's newly-created global centre for innovation.
We offer a blend of industrial-related applicative problems and academic-oriented activities, including an open publication policy.
Apply online here:
https://www.accenture.com/ie-en/careers/jobdetails?id=00784132_en
or feel free to contact Luca Costabello should you have any questions [2].
[1] https://github.com/Accenture/AmpliGraph
[2] https://luca.costabello.info/
✔️ @ApplyTime
Accenture Labs Dublin is looking for a Post-Doctoral researcher in the domain of Graph Representation Learning and Explainable AI.
You will join a newly-created, multi-partner project whose goal is to identify factors that can cause development of new medical conditions, and worsen the quality of life of cancer survivors. The project will analyse patient’s clinical, genomic, behavioural data and existing open data in order to determine a follow-up adapted to the individual needs. The length of the PostDoc is 3 years.
You will join our team of AI researchers and engineers, and work on research activities focused on explainable AI and machine learning on knowledge graphs.
You will be in charge of designing, developing, evaluating, and applying novel models. That will include software development (including contributing to our open source stack [1]), carrying out experiments, and publishing results in academic venues.
More precisely, you will design interpretable machine learning models to infer knowledge from a graph of clinical, genomic, and behavioural data. Explanations will use a wide range of techniques, such as rules derived from the deep learning models, interpretable machine learning, or graph-based explanations based on network analysis.
Requirements
* PhD in computer science, statistics, mathematics or related field.
* Proven communication skills (talks, presentations, academic publications)
* Strong foundation in mathematics, statistics and probability
* Strong knowledge of Machine Learning foundations
* Knowledge of mainstream Deep Learning architectures
* Ability to work creatively and analytically in a problem-solving environment
* Strong Python programming skills
* Hands-on experience with machine learning frameworks (e.g. TensorFlow, PyTorch), and scientific Python (e.g. numpy)
* Eagerness to contribute in a team-oriented environment
Preferred Qualifications
* Experience with graph-based knowledge representation (e.g. knowledge graphs)
* Familiarity with graph representation learning (e.g. knowledge graph embeddings, graph neural networks)
* Experience with explainable AI or interpretable machine learning techniques
* Working knowledge of Linux OS and shell noscripting
* Hands-on experience with git and issue tracking systems
About Accenture Labs Dublin
Accenture Labs is a network of R&D labs distributed on seven locations worldwide, home of over 200 applied R&D experts. The Dublin Labs team focuses on artificial intelligence, with a strong emphasis on explainable AI, machine learning on knowledge graphs (graph representation learning), and computational creativity. The lab is co-located with the over 100 designers, developers and domain experts at The Dock, Accenture's newly-created global centre for innovation.
We offer a blend of industrial-related applicative problems and academic-oriented activities, including an open publication policy.
Apply online here:
https://www.accenture.com/ie-en/careers/jobdetails?id=00784132_en
or feel free to contact Luca Costabello should you have any questions [2].
[1] https://github.com/Accenture/AmpliGraph
[2] https://luca.costabello.info/
✔️ @ApplyTime
Accenture
PostDoc Researcher - Graph Representation Learning and Explainable AI
Learn more about applying for PostDoc Researcher - Graph Representation Learning and Explainable AI position at Accenture.
Postdoc position in computational biology/bioinformatics
at the Department of Genomics & Immunoregulation at the Life & Medical Sciences (LIMES) Institute, starting in December 2019 for two years, with the possibility of extension, in the field of single cell trannoscriptome analysis.
The LIMES (Life and Medical Sciences) Institute is part of the Faculty of Mathematical and Natural Sciences at the University of Bonn. The Institute has been established to foster molecular research spanning from immunology to metabolism using a broad spectrum of cutting-edge technologies including state-of–the-art genomic technologies. In this regard, the Department for Genomics and Immunoregulation at the LIMES-Institute together with PRECISE at the DZNE Bonn has a Next-Generation Sequencing Facility and is now seeking scientist in the field of bioinformatics, computational biology and systems biology with focus in single cell trannoscriptome analysis. The successful candidate will be part of a team preprocessing and analyzing single cell Next-Generation Sequencing data and improving/developing computational approaches using state of the art tools and IT infrastructure.
Our requirements are
Enthusiasm to work in a thriving academic research environment.
Strong background in computational biology, bioinformatics, biomathematics, biostatistics.
Experience in the analysis of Next-Generation Sequencing data e.g. single cell RNA-Seq, bulk RNA-Seq and ATAC/ChIP-Seq.
Strong background in biology, preferable immunology.
PhD degree or equivalent.
An interest to work in an international environment.
A collaborative attitude.
Ability to work independently as well as in a team.
Computer programming as well as skills.
Experience in working in Linux based environments.
Very good communication and writing skills in English.
What we have to offer
Challenging research projects.
State of the art technologies to tackle important scientific and medical questions
A thriving interdisciplinary research environment at the LIMES Institute.
Fully established bioinformatics department.
Salary is paid according to German TV-L (E 13 100%).
The University of Bonn is an equal opportunities employer.
Complete applications in English should include a CV, a brief statement of research experiences and interests, list of publications and addresses of two referees should be submitted as a single pdf file to Professor Dr. Joachim L. Schultze (office.immunogenomics@uni-bonn.de) by 22th of November.
✔️ @ApplyTime
at the Department of Genomics & Immunoregulation at the Life & Medical Sciences (LIMES) Institute, starting in December 2019 for two years, with the possibility of extension, in the field of single cell trannoscriptome analysis.
The LIMES (Life and Medical Sciences) Institute is part of the Faculty of Mathematical and Natural Sciences at the University of Bonn. The Institute has been established to foster molecular research spanning from immunology to metabolism using a broad spectrum of cutting-edge technologies including state-of–the-art genomic technologies. In this regard, the Department for Genomics and Immunoregulation at the LIMES-Institute together with PRECISE at the DZNE Bonn has a Next-Generation Sequencing Facility and is now seeking scientist in the field of bioinformatics, computational biology and systems biology with focus in single cell trannoscriptome analysis. The successful candidate will be part of a team preprocessing and analyzing single cell Next-Generation Sequencing data and improving/developing computational approaches using state of the art tools and IT infrastructure.
Our requirements are
Enthusiasm to work in a thriving academic research environment.
Strong background in computational biology, bioinformatics, biomathematics, biostatistics.
Experience in the analysis of Next-Generation Sequencing data e.g. single cell RNA-Seq, bulk RNA-Seq and ATAC/ChIP-Seq.
Strong background in biology, preferable immunology.
PhD degree or equivalent.
An interest to work in an international environment.
A collaborative attitude.
Ability to work independently as well as in a team.
Computer programming as well as skills.
Experience in working in Linux based environments.
Very good communication and writing skills in English.
What we have to offer
Challenging research projects.
State of the art technologies to tackle important scientific and medical questions
A thriving interdisciplinary research environment at the LIMES Institute.
Fully established bioinformatics department.
Salary is paid according to German TV-L (E 13 100%).
The University of Bonn is an equal opportunities employer.
Complete applications in English should include a CV, a brief statement of research experiences and interests, list of publications and addresses of two referees should be submitted as a single pdf file to Professor Dr. Joachim L. Schultze (office.immunogenomics@uni-bonn.de) by 22th of November.
✔️ @ApplyTime
PhD position - Microbial Biotechnology
Forschungszentrum Jülich GmbH Jülich
Your Job:
Many biotechnological processes fail because the product imposes too much stress on the microbe. We aim to address this by utilizing naturally stress-resistant Pseudomonas bacteria in a collaborative project called NO-STRESS. You will investigate microbial stress tolerance mechanisms. Based on your findings, you will engineer new strains with higher tolerance to chemical stressors. You will evaluate these new strains through biocatalytic production of industrially relevant chemicals, using state-of-the-art production pathways available in our lab. Your methodical focus will be on genetic engineering, metabolic engineering, and laboratory evolution.
More info
✔️ @ApplyTime
Forschungszentrum Jülich GmbH Jülich
Your Job:
Many biotechnological processes fail because the product imposes too much stress on the microbe. We aim to address this by utilizing naturally stress-resistant Pseudomonas bacteria in a collaborative project called NO-STRESS. You will investigate microbial stress tolerance mechanisms. Based on your findings, you will engineer new strains with higher tolerance to chemical stressors. You will evaluate these new strains through biocatalytic production of industrially relevant chemicals, using state-of-the-art production pathways available in our lab. Your methodical focus will be on genetic engineering, metabolic engineering, and laboratory evolution.
More info
✔️ @ApplyTime
www.fz-juelich.de
Forschungszentrum Jülich - Current Vacancies - PhD position - Microbial Biotechnology
post-doctoral fellowship is available for 2D/2.5D/3D/4D radiology image processing in Bethesda, Maryland, USA
Position Denoscription:
A post-doctoral fellowship is available for 2D/2.5D/3D/4D radiology image processing in Bethesda, Maryland, USA. Specific interest areas are deep learning, image segmentation, modeling, visualization, pattern recognition, computer-aided diagnosis, multi-organ models, atlases and registration. In particular, advanced skills in image processing (computer vision, mathematical modeling, optimization, machine learning) are sought. The researcher will work closely with staff scientists, imaging specialists and clinicians and have access to state-of-the-art whole body MRI, MRI-PET, low-dose CT scanners, advanced graphics workstations and parallel processing/GPU clusters.
For additional information, please visit: https://www.cc.nih.gov/meet-our-doctors/rsummers.html.
Qualifications:
Ph.D. in Computer Science, Electrical Engineering, or related discipline with experience in Computer Vision, Machine Learning, or Image Understanding domain, along with successful demonstration of key responsibilities.
Strong theoretical and practical background in computer vision, image and video analysis, such as object detection and recognition, statistical pattern recognition, machine learning, sparse methods and applied optimization. Prior knowledge about medical imaging is a plus but not a must. Enthusiasm in solving real world clinical imaging problems using large datasets, and hands-on coding skills and ability in C++ and Matlab.
This appointment is for one year and is renewable thereafter on a periodic basis (up to five years).
To Apply:
Applications should include a CV, brief statement of research interests and three letters of reference. Email application materials to Dr. Ronald Summers at rms@nih.gov.
DHHS and NIH are Equal Opportunity Employers.
Regards,
Sheeraz Akram
https://www.linkedin.com/in/sheerazakram/
More info
✔️ @ApplyTime
Position Denoscription:
A post-doctoral fellowship is available for 2D/2.5D/3D/4D radiology image processing in Bethesda, Maryland, USA. Specific interest areas are deep learning, image segmentation, modeling, visualization, pattern recognition, computer-aided diagnosis, multi-organ models, atlases and registration. In particular, advanced skills in image processing (computer vision, mathematical modeling, optimization, machine learning) are sought. The researcher will work closely with staff scientists, imaging specialists and clinicians and have access to state-of-the-art whole body MRI, MRI-PET, low-dose CT scanners, advanced graphics workstations and parallel processing/GPU clusters.
For additional information, please visit: https://www.cc.nih.gov/meet-our-doctors/rsummers.html.
Qualifications:
Ph.D. in Computer Science, Electrical Engineering, or related discipline with experience in Computer Vision, Machine Learning, or Image Understanding domain, along with successful demonstration of key responsibilities.
Strong theoretical and practical background in computer vision, image and video analysis, such as object detection and recognition, statistical pattern recognition, machine learning, sparse methods and applied optimization. Prior knowledge about medical imaging is a plus but not a must. Enthusiasm in solving real world clinical imaging problems using large datasets, and hands-on coding skills and ability in C++ and Matlab.
This appointment is for one year and is renewable thereafter on a periodic basis (up to five years).
To Apply:
Applications should include a CV, brief statement of research interests and three letters of reference. Email application materials to Dr. Ronald Summers at rms@nih.gov.
DHHS and NIH are Equal Opportunity Employers.
Regards,
Sheeraz Akram
https://www.linkedin.com/in/sheerazakram/
More info
✔️ @ApplyTime
Neuroscience PhD Program, Tuebingen: deadline 15th Dec
The Max Planck Institute for Biological Cybernetics and the University
of Tübingen invite students from all over the world to apply for their
interdisciplinary 5-year PhD program leading to a PhD in
Neuroscience. Full funding will be available for top-ranked applicants.
We are seeking talented, curious and open-minded young scientists with
strong backgrounds in neuroscience, biomedical sciences, computational
science, applied mathematics, statistics, artificial intelligence, or
engineering. Successful candidates will possess a burning aspiration to
shape the future of neuroscience and the ability to thrive in a
fast-paced, interdisciplinary, environment.
The application deadline is December 15, 2019. Please visit:
https://www.kyb.tuebingen.mpg.de/phd-program
for more details and information about applying.
The PhD program is a collaboration between the Max Planck Institute for
Biological Cybernetics and the University of Tübingen. It is closely
affiliated with the renowned Graduate Training Centre of Neuroscience,
the centerpiece of neuroscience training in Tübingen. Students (who
should have been awarded a Bachelor's degree by September 2020) will
receive a broad interdisciplinary training in neuroscience, including
expert teaching by international renowned scientists and individual and
intensive mentoring.
Potential research topics cover a variety of fields in systems
neuroscience, cognitive and behavioral neuroscience, computational
neuroscience, translational and clinical neuroscience as well as
cellular and molecular neuroscience.
Teaching and research are conducted in English.
✔️ @ApplyTime
The Max Planck Institute for Biological Cybernetics and the University
of Tübingen invite students from all over the world to apply for their
interdisciplinary 5-year PhD program leading to a PhD in
Neuroscience. Full funding will be available for top-ranked applicants.
We are seeking talented, curious and open-minded young scientists with
strong backgrounds in neuroscience, biomedical sciences, computational
science, applied mathematics, statistics, artificial intelligence, or
engineering. Successful candidates will possess a burning aspiration to
shape the future of neuroscience and the ability to thrive in a
fast-paced, interdisciplinary, environment.
The application deadline is December 15, 2019. Please visit:
https://www.kyb.tuebingen.mpg.de/phd-program
for more details and information about applying.
The PhD program is a collaboration between the Max Planck Institute for
Biological Cybernetics and the University of Tübingen. It is closely
affiliated with the renowned Graduate Training Centre of Neuroscience,
the centerpiece of neuroscience training in Tübingen. Students (who
should have been awarded a Bachelor's degree by September 2020) will
receive a broad interdisciplinary training in neuroscience, including
expert teaching by international renowned scientists and individual and
intensive mentoring.
Potential research topics cover a variety of fields in systems
neuroscience, cognitive and behavioral neuroscience, computational
neuroscience, translational and clinical neuroscience as well as
cellular and molecular neuroscience.
Teaching and research are conducted in English.
✔️ @ApplyTime
Fully funded PhD Studentships in Games and Game AI (EPSRC, UK)
The IGGI Programme
The EPSRC Centre for Doctoral Training in Intelligent Games and Game Intelligence (IGGI) is the world's largest PhD research programme aimed at games.
IGGI has up to 12 studentships available for 2020/21 entry (starting September 2020) at the University of York or Queen Mary, University of London (QMUL). Studentships fund full fees (at a Home/EU rate) plus a tax-free living stipend, for a 4-year PhD programme.
The deadline for submitting applications for funded studentships is 23:59 GMT on 31st January 2020.
We also welcome self-funded applications throughout the year. All accepted IGGI students start at the beginning of the academic year in September.
See here for how to apply: http://www.iggi.org.uk/apply/
✔️ @ApplyTime
The IGGI Programme
The EPSRC Centre for Doctoral Training in Intelligent Games and Game Intelligence (IGGI) is the world's largest PhD research programme aimed at games.
IGGI has up to 12 studentships available for 2020/21 entry (starting September 2020) at the University of York or Queen Mary, University of London (QMUL). Studentships fund full fees (at a Home/EU rate) plus a tax-free living stipend, for a 4-year PhD programme.
The deadline for submitting applications for funded studentships is 23:59 GMT on 31st January 2020.
We also welcome self-funded applications throughout the year. All accepted IGGI students start at the beginning of the academic year in September.
See here for how to apply: http://www.iggi.org.uk/apply/
✔️ @ApplyTime
2.5 year postdoc at Lancaster University
We invite applications for a Senior Post-Doctoral Research Associate position to join the Next Generation Converged Digital infrastructure (NG-CDI) programme <http://www.ng-cdi.org> in the Department of Mathematics and Statistics at Lancaster University. NG-CDI is a £5m cross-disciplinary research project, jointly funded by BT and EPSRC, that will forge the foundational research needed to underpin the next generation converged digital infrastructure for the UK. The consortium brings together research groups from the Universities of Lancaster, Cambridge, Bristol and Surrey to work alongside BT, one of the world’s leading communications service providers.
The successful candidate on this 2.5 year post-doctoral position will work with David Leslie, developing recommender systems methodologies to suggest responses to anomalies detected on communications networks. You should have, or be close to completing, a PhD in Statistics, Machine Learning, Operations Research or a related discipline, with experience in one of more of the following areas: computational statistics; recommender systems; sequential decision-making.
Candidates who are considering making an application should find further details at <https://hr-jobs.lancs.ac.uk/Vacancy.aspx?ref=A2896>.
--
David Leslie, Professor of Statistical Learning,
Department of Mathematics and Statistics, Fylde College
Lancaster University, Lancaster, LA1 4YF, United Kingdom
d.leslie@lancaster.ac.uk
http://www.lancaster.ac.uk/fas/maths/people/david-leslie
✔️ @ApplyTime
We invite applications for a Senior Post-Doctoral Research Associate position to join the Next Generation Converged Digital infrastructure (NG-CDI) programme <http://www.ng-cdi.org> in the Department of Mathematics and Statistics at Lancaster University. NG-CDI is a £5m cross-disciplinary research project, jointly funded by BT and EPSRC, that will forge the foundational research needed to underpin the next generation converged digital infrastructure for the UK. The consortium brings together research groups from the Universities of Lancaster, Cambridge, Bristol and Surrey to work alongside BT, one of the world’s leading communications service providers.
The successful candidate on this 2.5 year post-doctoral position will work with David Leslie, developing recommender systems methodologies to suggest responses to anomalies detected on communications networks. You should have, or be close to completing, a PhD in Statistics, Machine Learning, Operations Research or a related discipline, with experience in one of more of the following areas: computational statistics; recommender systems; sequential decision-making.
Candidates who are considering making an application should find further details at <https://hr-jobs.lancs.ac.uk/Vacancy.aspx?ref=A2896>.
--
David Leslie, Professor of Statistical Learning,
Department of Mathematics and Statistics, Fylde College
Lancaster University, Lancaster, LA1 4YF, United Kingdom
d.leslie@lancaster.ac.uk
http://www.lancaster.ac.uk/fas/maths/people/david-leslie
✔️ @ApplyTime
Research Assistant: Speech, Language, and Cognition using Intracranial Recordings at Duke University
The Cogan Lab (PI: Gregory Cogan) at Duke University is seeking a research assistant to start ASAP (late 2019/Early 2020).
Research in the lab focuses on understanding the neural computations that underlie speech, language, and cognition. We use a combination of invasive recordings in adult and pediatric epilepsy patients: stereo-electroencephalography - SEEG, and electrocorticography – ECoG, and non-invasive recordings in healthy participants: electroencephalography – EEG. We also collaborate closely with the Viventi Lab (viventi.pratt.duke.edu - Department of Biomedical Engineering) to develop high density/channel count micro-electroencephalography (µECoG - < 2 mm spacing, up to 1024 channels) to record from functional neurosurgery patients (e.g. epilepsy, movement disorders, tumor patients) to better understand the micro-scale of human cognition. See the lab website for more information:
http://www.coganlab.org
Responsibilities include subject recruitment, data collection (including interacting with patients), data management, data analysis, helping to write papers for publication, and general administrivia. Working on weekends will also sometimes be necessary (with overtime pay).
The ideal candidate will be enthusiastic about a career in science/medicine/engineering, self-motivated, independent, creative, and have interest in speech/language, and/or cognitive/systems neuroscience. They will also have great interpersonal skills and a strong willingness to learn.
Candidates must have an undergraduate degree in a related field (e.g. Neuroscience, Psychology, Engineering, Biology, etc.), and experience with programming in Matlab and/or Python.
Duke University is an excellent and highly interdisciplinary place for research. Collaborators span multiple departments/institutes including the Duke Institute for Brain Sciences, the Center for Cognitive Neuroscience, Psychology and Neuroscience, Neurology, and Biomedical Engineering.
The position is open until filled. Interested applicants can send a CV, a brief statement of interest, as well as contact information for two references to: gregory [dot] cogan [at] duke [dot] edu
✔️ @ApplyTime
The Cogan Lab (PI: Gregory Cogan) at Duke University is seeking a research assistant to start ASAP (late 2019/Early 2020).
Research in the lab focuses on understanding the neural computations that underlie speech, language, and cognition. We use a combination of invasive recordings in adult and pediatric epilepsy patients: stereo-electroencephalography - SEEG, and electrocorticography – ECoG, and non-invasive recordings in healthy participants: electroencephalography – EEG. We also collaborate closely with the Viventi Lab (viventi.pratt.duke.edu - Department of Biomedical Engineering) to develop high density/channel count micro-electroencephalography (µECoG - < 2 mm spacing, up to 1024 channels) to record from functional neurosurgery patients (e.g. epilepsy, movement disorders, tumor patients) to better understand the micro-scale of human cognition. See the lab website for more information:
http://www.coganlab.org
Responsibilities include subject recruitment, data collection (including interacting with patients), data management, data analysis, helping to write papers for publication, and general administrivia. Working on weekends will also sometimes be necessary (with overtime pay).
The ideal candidate will be enthusiastic about a career in science/medicine/engineering, self-motivated, independent, creative, and have interest in speech/language, and/or cognitive/systems neuroscience. They will also have great interpersonal skills and a strong willingness to learn.
Candidates must have an undergraduate degree in a related field (e.g. Neuroscience, Psychology, Engineering, Biology, etc.), and experience with programming in Matlab and/or Python.
Duke University is an excellent and highly interdisciplinary place for research. Collaborators span multiple departments/institutes including the Duke Institute for Brain Sciences, the Center for Cognitive Neuroscience, Psychology and Neuroscience, Neurology, and Biomedical Engineering.
The position is open until filled. Interested applicants can send a CV, a brief statement of interest, as well as contact information for two references to: gregory [dot] cogan [at] duke [dot] edu
✔️ @ApplyTime
Cogan Lab
The Cogan Lab at Duke University: Cognitive Neuroscience of Speech
The Cogan Lab at Duke University: Using invasive human electrophysiology to study speech, language, and cognition
Open Research Position (PhD / PostDoc) in Biomedical Data Integration, Information Extraction, and Variant Assessment
The research group for „Knowledge Management in Bioinformatics“ at Humboldt-Universität zu Berlin has an open 3-year research position.
The position is part of the DFG-funded research unit "Beyond the Exome", a collaboration between the Charite Berlin, the MDC Berlin, and Humboldt-Universität. The research unit investigates the role of non-coding variations for rare diseases. The position is paid according to TVL-E13 (roughly 2.200 Euro / month), at the earliest possible start date and will run for three years. Our prime interest is to find a highly motivated PhD student, but applications from PostDocs are also ligible.
Applicants are expected to pursue research in the fields of biomedical data integration, information integration, and variant assessment. We will build an integrated resource of information on non-coding variations and their relationships to diseases. An important source of information will be the scientific literature, for which we develop semi-automated methods for information extraction and normalization. The gathered information will be used to predict the clinical relevance of variations. The holder of this position will be able to chose its own focus within these topics (integration, extraction, prediction).
The position has no teaching duties attached. Therefore, speaking German is not a pre-requite.
More information can be found at https://www.informatik.hu-berlin.de/wbi/jobs/wimi_1912.html
Sincerely,
Ulf Leser
✔️ @ApplyTime
The research group for „Knowledge Management in Bioinformatics“ at Humboldt-Universität zu Berlin has an open 3-year research position.
The position is part of the DFG-funded research unit "Beyond the Exome", a collaboration between the Charite Berlin, the MDC Berlin, and Humboldt-Universität. The research unit investigates the role of non-coding variations for rare diseases. The position is paid according to TVL-E13 (roughly 2.200 Euro / month), at the earliest possible start date and will run for three years. Our prime interest is to find a highly motivated PhD student, but applications from PostDocs are also ligible.
Applicants are expected to pursue research in the fields of biomedical data integration, information integration, and variant assessment. We will build an integrated resource of information on non-coding variations and their relationships to diseases. An important source of information will be the scientific literature, for which we develop semi-automated methods for information extraction and normalization. The gathered information will be used to predict the clinical relevance of variations. The holder of this position will be able to chose its own focus within these topics (integration, extraction, prediction).
The position has no teaching duties attached. Therefore, speaking German is not a pre-requite.
More information can be found at https://www.informatik.hu-berlin.de/wbi/jobs/wimi_1912.html
Sincerely,
Ulf Leser
✔️ @ApplyTime
Fully funded PhD candidate positions in Organismal Biology
IMPRS for Organismal Biology Radolfzell am Bodensee
Job denoscription
The International Max Planck Research School for Organismal Biology (IMPRS), a joint cooperation between the Max Planck Institute for Ornithology in Seewiesen, the newly founded Max Planck Institute of Animal Behavior with two research sites in Radolfzell and Konstanz, and the Department of Biology at the University of Konstanz, are seeking doctoral candidates (f/m/d). For 2020, the IMPRS announces various fully-funded doctoral projects (3 years) focusing on collective behavior, ecology, neuroethology, social structure, using quantitative analysis and virtual reality to name but a few of the exciting techniques used to answer research questions in the field of Organismal Biology (see https://www.orn.mpg.de/projects)!
Activities and responsibilities
The aim of the IMPRS is to provide first-class training and education for outstanding doctoral students from all over the world in a stimulating research environment. More than 40 scientists work on a variety of topics from animal migration, collective behavior, computational ecology, evolutionary genetics, neurobiology, sensory systems, social interactions and other related fields. Research includes lab and field work using different model organisms from plants, insects, fish, birds and mammals, as well as diverse techniques from animal tracking, large scale data analysis, molecular genetic analyses, optical imaging or social network analysis.
Qualification profile
We invite applications from all countries and from a wide range of backgrounds (biology, engineering science, physics, computer science). Applicants must have graduated with a Master's degree and/or an equivalent degree, in exceptional cases candidates with a 4-year study program BSc with honors (with a comprehensive scientific thesis work), shall be considered. It is not necessary to hold the final degree(s) at the time of the application. However, you must have been awarded your degree prior to the start of the program.
The standard of the degrees (i.e. eligibility) will be checked by University of Konstanz on a case-by-case basis.
Candidates need to be fluent in written and spoken English and - unless native speakers - have to document their proficiency in English (e.g. TOEFL).
We offer
All doctoral projects are fully funded for at least 3 years. On top of their own research, the IMPRS fellows attend laboratory courses and workshops in relevant transferable skills such as scientific writing and project management. Talks by invited speakers during our annual IMPRS symposium, student retreats, and conference participation complete the individually tailored curriculum.
Send application to
You can only apply via the three-tier electronical application process on the Institutes webpage. Please do not send any other type of application by regular mail or email as they will be rejected. The application must be completed in English only. Besides the online application form, we need several documents from you. Documents that are not in English or German need to be translated. You need to upload all the required documents as a single PDF-file.
The Max Planck Society and the University of Konstanz are equal opportunity employers and are committed to increasing the number of individuals with disabilities in their workforce and therefore we encourage applications from such qualified individuals. Furthermore, we seek to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply.
Application deadline is January 15, 2020. Please only apply via http://www.orn.mpg.de/2383/Application.
✔️ @ApplyTime
IMPRS for Organismal Biology Radolfzell am Bodensee
Job denoscription
The International Max Planck Research School for Organismal Biology (IMPRS), a joint cooperation between the Max Planck Institute for Ornithology in Seewiesen, the newly founded Max Planck Institute of Animal Behavior with two research sites in Radolfzell and Konstanz, and the Department of Biology at the University of Konstanz, are seeking doctoral candidates (f/m/d). For 2020, the IMPRS announces various fully-funded doctoral projects (3 years) focusing on collective behavior, ecology, neuroethology, social structure, using quantitative analysis and virtual reality to name but a few of the exciting techniques used to answer research questions in the field of Organismal Biology (see https://www.orn.mpg.de/projects)!
Activities and responsibilities
The aim of the IMPRS is to provide first-class training and education for outstanding doctoral students from all over the world in a stimulating research environment. More than 40 scientists work on a variety of topics from animal migration, collective behavior, computational ecology, evolutionary genetics, neurobiology, sensory systems, social interactions and other related fields. Research includes lab and field work using different model organisms from plants, insects, fish, birds and mammals, as well as diverse techniques from animal tracking, large scale data analysis, molecular genetic analyses, optical imaging or social network analysis.
Qualification profile
We invite applications from all countries and from a wide range of backgrounds (biology, engineering science, physics, computer science). Applicants must have graduated with a Master's degree and/or an equivalent degree, in exceptional cases candidates with a 4-year study program BSc with honors (with a comprehensive scientific thesis work), shall be considered. It is not necessary to hold the final degree(s) at the time of the application. However, you must have been awarded your degree prior to the start of the program.
The standard of the degrees (i.e. eligibility) will be checked by University of Konstanz on a case-by-case basis.
Candidates need to be fluent in written and spoken English and - unless native speakers - have to document their proficiency in English (e.g. TOEFL).
We offer
All doctoral projects are fully funded for at least 3 years. On top of their own research, the IMPRS fellows attend laboratory courses and workshops in relevant transferable skills such as scientific writing and project management. Talks by invited speakers during our annual IMPRS symposium, student retreats, and conference participation complete the individually tailored curriculum.
Send application to
You can only apply via the three-tier electronical application process on the Institutes webpage. Please do not send any other type of application by regular mail or email as they will be rejected. The application must be completed in English only. Besides the online application form, we need several documents from you. Documents that are not in English or German need to be translated. You need to upload all the required documents as a single PDF-file.
The Max Planck Society and the University of Konstanz are equal opportunity employers and are committed to increasing the number of individuals with disabilities in their workforce and therefore we encourage applications from such qualified individuals. Furthermore, we seek to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply.
Application deadline is January 15, 2020. Please only apply via http://www.orn.mpg.de/2383/Application.
✔️ @ApplyTime
PhD and Postdoc Positions in Machine Learning and Image Analysis at Oregon Health and Science University
Machine learning and deep learning are changing how biomedical research is conducted and how patients are diagnosed and treated. Computational image analysis leverages large-scale machine learning on high performance computing infrastructure to transform qualitative science to quantitative science. We develop innovative machine learning and computer vision algorithms and apply them to biomedicine so that they are used by biomedical researchers and physicians in the clinic. First and foremost, you will be trained to become a machine learning scientist (most of our graduates work in high-tech companies such as Microsoft, Nvidia, Intel, Apple, and Amazon). Furthermore, you will work in an exciting interdisciplinary environment with the potential to directly impact medical research, cancer care and patients’ lives.
You Will:
Work and collaborate with a diverse team of machine learning experts, imaging scientists, cancer biologists, radiologists and medical doctors to build a new generation of artificial intelligence in cancer research, detection and treatment.
Create algorithms and software in machine learning and deep learning for quantitative image analysis that enable discoveries in biomedicine and the evaluation of biological variation in diseases such as cancer. The tasks may include image registration, segmentation, characterization, quantification, and analysis. The imaging modalities may include electron microscopy, cyclic immunofluorescence, MRI and CT.
Leverage the unique opportunity within the field of computer vision and cancer research provided by the massive amount of image data at OHSU for conventional and unconventional modeling with clinical and biomedical relevance, such as (semi-)supervised, weakly supervised and unsupervised machine learning methods.
Have the opportunity to leverage modern computing clusters with hundreds of GPU’s and large cluster of DGX nodes.
Required Skills:
Bachelor’s or Master’s Degree in Computer Science, Electrical Engineering, Biomedical Engineering, Math, Physics, or other engineering and science field.
Strong analytical and computing skills.
Proficiency in advanced programming languages such as Python, Matlab, C/C++ or Java.
Interest in machine learning & deep learning and their application in medicine.
Knowledge about cancer biology and microscopic imaging is a plus.
OSHU is located in Portland, Oregon, in the Pacific Northwest. It is in close proximity of the “Silicon Forest” that consists of many high-tech companies. Portland boasts a thriving biotech scene with many biomedical companies, and is known for its mild climate, natural beauty, access to outdoors, and quality of living. Please send your CV to songx@oshu.edu.
✔️ @ApplyTime
Machine learning and deep learning are changing how biomedical research is conducted and how patients are diagnosed and treated. Computational image analysis leverages large-scale machine learning on high performance computing infrastructure to transform qualitative science to quantitative science. We develop innovative machine learning and computer vision algorithms and apply them to biomedicine so that they are used by biomedical researchers and physicians in the clinic. First and foremost, you will be trained to become a machine learning scientist (most of our graduates work in high-tech companies such as Microsoft, Nvidia, Intel, Apple, and Amazon). Furthermore, you will work in an exciting interdisciplinary environment with the potential to directly impact medical research, cancer care and patients’ lives.
You Will:
Work and collaborate with a diverse team of machine learning experts, imaging scientists, cancer biologists, radiologists and medical doctors to build a new generation of artificial intelligence in cancer research, detection and treatment.
Create algorithms and software in machine learning and deep learning for quantitative image analysis that enable discoveries in biomedicine and the evaluation of biological variation in diseases such as cancer. The tasks may include image registration, segmentation, characterization, quantification, and analysis. The imaging modalities may include electron microscopy, cyclic immunofluorescence, MRI and CT.
Leverage the unique opportunity within the field of computer vision and cancer research provided by the massive amount of image data at OHSU for conventional and unconventional modeling with clinical and biomedical relevance, such as (semi-)supervised, weakly supervised and unsupervised machine learning methods.
Have the opportunity to leverage modern computing clusters with hundreds of GPU’s and large cluster of DGX nodes.
Required Skills:
Bachelor’s or Master’s Degree in Computer Science, Electrical Engineering, Biomedical Engineering, Math, Physics, or other engineering and science field.
Strong analytical and computing skills.
Proficiency in advanced programming languages such as Python, Matlab, C/C++ or Java.
Interest in machine learning & deep learning and their application in medicine.
Knowledge about cancer biology and microscopic imaging is a plus.
OSHU is located in Portland, Oregon, in the Pacific Northwest. It is in close proximity of the “Silicon Forest” that consists of many high-tech companies. Portland boasts a thriving biotech scene with many biomedical companies, and is known for its mild climate, natural beauty, access to outdoors, and quality of living. Please send your CV to songx@oshu.edu.
✔️ @ApplyTime
PhD in modelling of heterogeneous medical data for clinical impact
The chronic lymphocytic leukemia (CLL) lab at Rigshospitalet, invite applications for an appointment as PhD student (health sciences, Copenhagen University) in modelling of heterogeneous medical data for clinical impact. The position is available from January, 2020 or according to mutual agreement.
Responsibilities and tasks
Machine learning on medical data has several challenges that need to be overcome for clinical implementation. This includes making predictions with low sample sizes, high dimensions and missing data. In this PhD you will be at the forefront of defining methodologies for handling missing data, combining multiple data-sources and providing trustable predictions for the clinic. You will carry out collaborative work with high profile partners at Technical University of Denmark (DTU), Institute Pasteur and PERSIMUNE.org for addressing key research questions related to chronic lymphocytic leukemia (CLL) and other lymphoproliferative malignancies. The research will focus on providing clinical value using ML strategies and includes:
Novel machine learning strategies for combining multidimensional clinical data from various sources such as flow cytometry, next generation DNA sequencing, gene expression & functional assays, baseline clinical, and para-clinical data.
Novel machine learning methods for cleaning and organizing clinical data with least bias.
Effective handling of missing data and low sample sizes.
Methods for interpretation of trained machine learning models with clinical viability in mind.
Publishing on High-Impact factor journals (NEJM, Blood, Nature Comm.)
Effective communication with medical doctors on modelling strategy choices and results.
Research environment
We offer a challenging PhD in an international environment. We value knowledge sharing and multi-disciplinarity. Hence, you are expected to cooperate and be eager to build relations with colleagues within the department and with collaboration partners. Through our collaboration with PERSIMUNE, you will be uniquely positioned to access rich and carefully curated datasets that are unattainable anywhere else in the world. Additionally, through our collaboration with DTU, you will have access to the high-performance computing (HPC) clusters for computational requirements. Our research group has the setup needed for fast testing in clinical trials through international collaborations (PreVent-ACaLL trial, clinicaltrials.gov: NCT03868722), therefore there is ample opportunity for your work as a modeller to have direct clinical impact. Our current research-group includes 5 scientific researchers (4 PhDs and 1 Post-Doc), 3 laboratory technicians, 20 clinical trial unit employees at the Department of Hematology, and 50+ data cleaning and structural IT technicians at the PERSIMUNE data lake. We strive for academic excellence, collegial respect and freedom tempered by responsibility. We also believe in each individual’s patterns of productivity. We welcome flexible time and location of working hours, as long as the quota for the full-time work week is reached.
More info
✔️ @ApplyTime
The chronic lymphocytic leukemia (CLL) lab at Rigshospitalet, invite applications for an appointment as PhD student (health sciences, Copenhagen University) in modelling of heterogeneous medical data for clinical impact. The position is available from January, 2020 or according to mutual agreement.
Responsibilities and tasks
Machine learning on medical data has several challenges that need to be overcome for clinical implementation. This includes making predictions with low sample sizes, high dimensions and missing data. In this PhD you will be at the forefront of defining methodologies for handling missing data, combining multiple data-sources and providing trustable predictions for the clinic. You will carry out collaborative work with high profile partners at Technical University of Denmark (DTU), Institute Pasteur and PERSIMUNE.org for addressing key research questions related to chronic lymphocytic leukemia (CLL) and other lymphoproliferative malignancies. The research will focus on providing clinical value using ML strategies and includes:
Novel machine learning strategies for combining multidimensional clinical data from various sources such as flow cytometry, next generation DNA sequencing, gene expression & functional assays, baseline clinical, and para-clinical data.
Novel machine learning methods for cleaning and organizing clinical data with least bias.
Effective handling of missing data and low sample sizes.
Methods for interpretation of trained machine learning models with clinical viability in mind.
Publishing on High-Impact factor journals (NEJM, Blood, Nature Comm.)
Effective communication with medical doctors on modelling strategy choices and results.
Research environment
We offer a challenging PhD in an international environment. We value knowledge sharing and multi-disciplinarity. Hence, you are expected to cooperate and be eager to build relations with colleagues within the department and with collaboration partners. Through our collaboration with PERSIMUNE, you will be uniquely positioned to access rich and carefully curated datasets that are unattainable anywhere else in the world. Additionally, through our collaboration with DTU, you will have access to the high-performance computing (HPC) clusters for computational requirements. Our research group has the setup needed for fast testing in clinical trials through international collaborations (PreVent-ACaLL trial, clinicaltrials.gov: NCT03868722), therefore there is ample opportunity for your work as a modeller to have direct clinical impact. Our current research-group includes 5 scientific researchers (4 PhDs and 1 Post-Doc), 3 laboratory technicians, 20 clinical trial unit employees at the Department of Hematology, and 50+ data cleaning and structural IT technicians at the PERSIMUNE data lake. We strive for academic excellence, collegial respect and freedom tempered by responsibility. We also believe in each individual’s patterns of productivity. We welcome flexible time and location of working hours, as long as the quota for the full-time work week is reached.
More info
✔️ @ApplyTime
Postdoc Computer Science, Computational Biology - Next Generation Sequencing Data Analysis (m/f/d)
Genomik und Immunregulation (LIMES) Bonn
PRECISE – Platform for Single Cell Genomics and Epigenomics
Experienced postdoc in Computer Science
The PRECISE – Platform for Single Cell Genomics and Epigenomics is part of the German Center for Neurodegenerative Diseases (DZNE) and the LIMES (Life and Medical Sciences) Institute at the University of Bonn. PRECISE uses high throughput technologies such as liquid handling robots and next generation sequencing (NGS), to answer basic and translational scientific questions in the area of neurodegenerative diseases and immunology together with local, national and international collaboration partners. PRECISE, in collaboration with industrial partners, offers access to novel, cutting-edge computer architectures focusing on the memory centric approaches. The successful candidate will be part of a team working on life science algorithms and computational approaches for Memory-Driven Computing to analyze next generation sequencing data and integrate NGS based datasets in a systems biology context. Key parts of the work include working with sparse data sets and developing novel algorithms that take advantage of PRECISE’s highly parallel Memory-Driven Computing systems.
Our requirements are
PhD or equivalent in computer science or related fields.
Experience in computational biology, bioinformatics or biomathematics is a plus.
Knowledge and expertise in working with sparse data.
Solid experience in object-oriented software development, ideally with C++ and python.
Experience in parallel computing and in-memory approaches is of advantage.
Enthusiasm to work in a thriving academic research environment.
An interest to work in an international environment.
A collaborative attitude and the ability to work independently as well as in a team.
Excellent verbal and written communication skills in English.
What we have to offer
Participation in a challenging research project
State of the art technologies to tackle important scientific and medical questions
A thriving interdisciplinary research environment at the DZNE
Fully established bioinformatics department
Memory-Driven Computing hardware resources
Close collaboration with industry partners and early access to novel developments
Salary is paid according to German TV-öD (E 13, 100%); the position is initially limited to two years.
The DZNE and the University of Bonn are equal opportunities employers.
Applications (in English) should include a CV, a brief statement of research experiences and interests, a list of publications (if applicable) and the addresses of two referees, the Reference number LIMES112019 and should be submitted as a single pdf file to Professor Dr. Joachim L. Schultze (office.immunogenomics@uni-bonn.de) by 01st of December, 2019. We are looking to fill this position as soon as possible.
✔️ @ApplyTime
Genomik und Immunregulation (LIMES) Bonn
PRECISE – Platform for Single Cell Genomics and Epigenomics
Experienced postdoc in Computer Science
The PRECISE – Platform for Single Cell Genomics and Epigenomics is part of the German Center for Neurodegenerative Diseases (DZNE) and the LIMES (Life and Medical Sciences) Institute at the University of Bonn. PRECISE uses high throughput technologies such as liquid handling robots and next generation sequencing (NGS), to answer basic and translational scientific questions in the area of neurodegenerative diseases and immunology together with local, national and international collaboration partners. PRECISE, in collaboration with industrial partners, offers access to novel, cutting-edge computer architectures focusing on the memory centric approaches. The successful candidate will be part of a team working on life science algorithms and computational approaches for Memory-Driven Computing to analyze next generation sequencing data and integrate NGS based datasets in a systems biology context. Key parts of the work include working with sparse data sets and developing novel algorithms that take advantage of PRECISE’s highly parallel Memory-Driven Computing systems.
Our requirements are
PhD or equivalent in computer science or related fields.
Experience in computational biology, bioinformatics or biomathematics is a plus.
Knowledge and expertise in working with sparse data.
Solid experience in object-oriented software development, ideally with C++ and python.
Experience in parallel computing and in-memory approaches is of advantage.
Enthusiasm to work in a thriving academic research environment.
An interest to work in an international environment.
A collaborative attitude and the ability to work independently as well as in a team.
Excellent verbal and written communication skills in English.
What we have to offer
Participation in a challenging research project
State of the art technologies to tackle important scientific and medical questions
A thriving interdisciplinary research environment at the DZNE
Fully established bioinformatics department
Memory-Driven Computing hardware resources
Close collaboration with industry partners and early access to novel developments
Salary is paid according to German TV-öD (E 13, 100%); the position is initially limited to two years.
The DZNE and the University of Bonn are equal opportunities employers.
Applications (in English) should include a CV, a brief statement of research experiences and interests, a list of publications (if applicable) and the addresses of two referees, the Reference number LIMES112019 and should be submitted as a single pdf file to Professor Dr. Joachim L. Schultze (office.immunogenomics@uni-bonn.de) by 01st of December, 2019. We are looking to fill this position as soon as possible.
✔️ @ApplyTime
Multiple PhD positions on machine learning with simulation and physics modeling of the world
We have several PhD openings in machine learning research for exploring methods to combine learning with process-driven modeling and simulations.
The successful candidate will enroll as a PhD student in the Computer Science department of the University of Geneva (under the co-direction of myself and Prof. Stephane Marchand-Maillet) and, at the same time, will become a member of the Data Mining and Machine Learning group (http://dmml.ch) as a research and teaching assistant at HES-SO, Geneva. The positions shall be filled in as soon as possible.
The interaction and cooperation between a simulator and a machine learning model can be exploited in a number of areas where data are expensive or difficult to obtain, and/or where domain knowledge within the process-driven models can back the inductive biases factored into the machine learning models.
In the medical domain, machine learning methods can be combined with neuromechanical simulators to develop models of human locomotion that shall support critical medical decisions related to surgical interventions treating pathological gait patterns. In industrial manufacturing, simulations and physical modeling of realistic or extreme operational conditions can support the learning of rare faulty behaviours in order to trigger early alerts. In chemoinformatics, an external system (e.g. RDKit) can provide relevant constraints for generating valid new molecules with specific required characteristics.
Related literature:
- Battaglia, Peter, et al. "Interaction networks for learning about objects, relations and physics." Advances in neural information processing systems. 2016.
- Lionel Blondé, Alexandros Kalousis "Sample-Efficient Imitation Learning via Generative Adversarial Nets." AISTATS 2019: 3138-3148
- Narayanaswamy, Siddharth, et al. "Learning disentangled representations with semi-supervised deep generative models." Advances in Neural Information Processing Systems. 2017.
We seek strongly motivated candidates prepared to dedicate to high quality research in the above domains for a number of years (the expected time to PhD graduation is 4-5 years). The candidate should have (or be close to obtaining) a Master's degree or equivalent in computer science, statistics, applied mathematics, electrical engineering or other related field with strong background in as many as possible (but at least some) of these: machine learning, probability and statistical modeling, mathematical optimization, programming and software development (preferably Pytorch and/or Tensorflow).
If interested, please send the following to alexandros.kalousis@hesge.ch
- academic CV (max 2 pages)
- academic trannoscript of the study results
- one page motivation letter explaining why the candidate is suitable for the position
- 500 word research proposal on one of the topics described above
- contact details of three referees (do not send reference letters)
The applications will be processed as they come as of now until the positions are filled. The status of the openings will be update here: http://dmml.ch/recruitment/
In case of any further questions, please contact alexandros.kalousis@hesge.ch. I will also be in NeurIPS/Vancouver so ping me if you are around.
✔️ @ApplyTime
We have several PhD openings in machine learning research for exploring methods to combine learning with process-driven modeling and simulations.
The successful candidate will enroll as a PhD student in the Computer Science department of the University of Geneva (under the co-direction of myself and Prof. Stephane Marchand-Maillet) and, at the same time, will become a member of the Data Mining and Machine Learning group (http://dmml.ch) as a research and teaching assistant at HES-SO, Geneva. The positions shall be filled in as soon as possible.
The interaction and cooperation between a simulator and a machine learning model can be exploited in a number of areas where data are expensive or difficult to obtain, and/or where domain knowledge within the process-driven models can back the inductive biases factored into the machine learning models.
In the medical domain, machine learning methods can be combined with neuromechanical simulators to develop models of human locomotion that shall support critical medical decisions related to surgical interventions treating pathological gait patterns. In industrial manufacturing, simulations and physical modeling of realistic or extreme operational conditions can support the learning of rare faulty behaviours in order to trigger early alerts. In chemoinformatics, an external system (e.g. RDKit) can provide relevant constraints for generating valid new molecules with specific required characteristics.
Related literature:
- Battaglia, Peter, et al. "Interaction networks for learning about objects, relations and physics." Advances in neural information processing systems. 2016.
- Lionel Blondé, Alexandros Kalousis "Sample-Efficient Imitation Learning via Generative Adversarial Nets." AISTATS 2019: 3138-3148
- Narayanaswamy, Siddharth, et al. "Learning disentangled representations with semi-supervised deep generative models." Advances in Neural Information Processing Systems. 2017.
We seek strongly motivated candidates prepared to dedicate to high quality research in the above domains for a number of years (the expected time to PhD graduation is 4-5 years). The candidate should have (or be close to obtaining) a Master's degree or equivalent in computer science, statistics, applied mathematics, electrical engineering or other related field with strong background in as many as possible (but at least some) of these: machine learning, probability and statistical modeling, mathematical optimization, programming and software development (preferably Pytorch and/or Tensorflow).
If interested, please send the following to alexandros.kalousis@hesge.ch
- academic CV (max 2 pages)
- academic trannoscript of the study results
- one page motivation letter explaining why the candidate is suitable for the position
- 500 word research proposal on one of the topics described above
- contact details of three referees (do not send reference letters)
The applications will be processed as they come as of now until the positions are filled. The status of the openings will be update here: http://dmml.ch/recruitment/
In case of any further questions, please contact alexandros.kalousis@hesge.ch. I will also be in NeurIPS/Vancouver so ping me if you are around.
✔️ @ApplyTime
Data mining and machine learning group, Geneva
About - Data mining and machine learning group, Geneva
We are a machine learning reserach lab based in Geneva. In our research, we focus on various modern ML problems including deep and reinforcement learning.
Jobs for PostDocs & PhD Students at the Swiss AI Lab, IDSIA
We intend to interview prospective
PhD students and postdocs
at NeurIPS 2019 in Vancouver.
Please find application instructions under
http://people.idsia.ch/~juergen/erc2017.html
NNAISENSE will also be present, and is also hiring:
https://nnaisense.com/careers.html
Jürgen Schmidhuber
Scientific Director, Swiss AI Lab, IDSIA
Professor of AI, USI & SUPSI, Switzerland
Chief Scientist, NNAISENSE
http://people.idsia.ch/~juergen/whatsnew.html
✔️ @ApplyTime
We intend to interview prospective
PhD students and postdocs
at NeurIPS 2019 in Vancouver.
Please find application instructions under
http://people.idsia.ch/~juergen/erc2017.html
NNAISENSE will also be present, and is also hiring:
https://nnaisense.com/careers.html
Jürgen Schmidhuber
Scientific Director, Swiss AI Lab, IDSIA
Professor of AI, USI & SUPSI, Switzerland
Chief Scientist, NNAISENSE
http://people.idsia.ch/~juergen/whatsnew.html
✔️ @ApplyTime
PhD (m/f/d) for the Division of Veterinary Medicine
Paul-Ehrlich-Institut Langen
About the Paul-Ehrlich-Institut
We are a senior federal authority responsible for vaccines and biomedicines that performs scientific research in the field of life sciences. Our institute networks with national and international players which makes it a competent partner for the general public, science, medicine, politics, and economics.
The Research Group „Gene Modification in Stem Cells“ at the Division of Veterinary Medicine studies hematopoietic gene therapy.
Your Key responsibilities
In the research project we will address basic questions of hematopoietic stem cell biology with the aim to translate the findings onto hematopoietic cell and gene therapy. Specifically, the role of selected thrombopoietin target genes in regeneration will be investigated.
Lentiviral expression of thrombopoietin target genes in hematopoietic stem cells and transplantation in relevant mouse models; Generation of inducible retro / lentiviral vectors for time-controlled expression.
Analysis of hematopoiesis in mice with gene modified blood cells (blood cell analysis, multi-color flow cytometry, histopathology, cytomorphology, molecular analysis).
Confocal microscopy of hematopoietic cells.
Use of a lentiviral barcode library and „next generation sequencing“ (NGS) for the analysis of hematopoietic clonality in vivo.
More info
✔️ @ApplyTime
Paul-Ehrlich-Institut Langen
About the Paul-Ehrlich-Institut
We are a senior federal authority responsible for vaccines and biomedicines that performs scientific research in the field of life sciences. Our institute networks with national and international players which makes it a competent partner for the general public, science, medicine, politics, and economics.
The Research Group „Gene Modification in Stem Cells“ at the Division of Veterinary Medicine studies hematopoietic gene therapy.
Your Key responsibilities
In the research project we will address basic questions of hematopoietic stem cell biology with the aim to translate the findings onto hematopoietic cell and gene therapy. Specifically, the role of selected thrombopoietin target genes in regeneration will be investigated.
Lentiviral expression of thrombopoietin target genes in hematopoietic stem cells and transplantation in relevant mouse models; Generation of inducible retro / lentiviral vectors for time-controlled expression.
Analysis of hematopoiesis in mice with gene modified blood cells (blood cell analysis, multi-color flow cytometry, histopathology, cytomorphology, molecular analysis).
Confocal microscopy of hematopoietic cells.
Use of a lentiviral barcode library and „next generation sequencing“ (NGS) for the analysis of hematopoietic clonality in vivo.
More info
✔️ @ApplyTime
Fully funded PhD position on Multimodal Machine Learning for Mental Health (CNRS GREYC, France)
Hi,
The Human Language Technology research group of the CNRS GREYC Laboratory (https://www.greyc.fr) invites applications for one fully funded three-year PhD position in Multimodal (acoustics, visual, text) Machine Learning for Mental Health.
The CNRS GREYC Laboratory carries out research activities in the field of digital sciences covering several aspects of computer science including image processing, data mining, artificial intelligence, computer security, mathematical computing, natural language processing, electronics and instrumentation. The laboratory gathers more than 200 members in Caen, Normandy.
The successful candidate must hold a Master degree or equivalent in Data Science, Applied Mathematics or Computer Science. A strong background in (statistical, deep) machine learning is required as well as knowledge in natural language processing or/and computer vision.
If you are interested in this position, please contact with a detailed cv at mohammed.hasanuzzaman@adaptcentre.ie or gael.dias@unicaen.fr
Best regards,
Mohammed Hasanuzzaman
✔️ @ApplyTime
Hi,
The Human Language Technology research group of the CNRS GREYC Laboratory (https://www.greyc.fr) invites applications for one fully funded three-year PhD position in Multimodal (acoustics, visual, text) Machine Learning for Mental Health.
The CNRS GREYC Laboratory carries out research activities in the field of digital sciences covering several aspects of computer science including image processing, data mining, artificial intelligence, computer security, mathematical computing, natural language processing, electronics and instrumentation. The laboratory gathers more than 200 members in Caen, Normandy.
The successful candidate must hold a Master degree or equivalent in Data Science, Applied Mathematics or Computer Science. A strong background in (statistical, deep) machine learning is required as well as knowledge in natural language processing or/and computer vision.
If you are interested in this position, please contact with a detailed cv at mohammed.hasanuzzaman@adaptcentre.ie or gael.dias@unicaen.fr
Best regards,
Mohammed Hasanuzzaman
✔️ @ApplyTime