Postdoc position in Machine Learning, Computational and Clinical Neuroscience at UCSF
Hello everyone,
I'm looking for a postdoc to work with us at UCSF on a project at the intersection of machine learning, computational and clinical neuroscience. The project seeks to develop quantitative methods to analyze multi-modal data from patients with neurological disease. This involves predictive modeling, interpretation inference, artificial neural networks, biomedical image and signal processing, visualization, etc. This is an opportunity to work on a highly impactful real-world problem within an interdisciplinary and collaborative environment.
Attached, you can find more information about the position.
Looking forward to hearing from you!
Reza
--
Reza Abbasi-Asl, PhD
Assistant Professor | Department of Neurology
Director of Data Analytics and Visualization | Weill Institute for Neurosciences
University of California, San Francisco
Reza.AbbasiAsl@ucsf.edu | abbasilab.org
✔️ @ApplyTime
Hello everyone,
I'm looking for a postdoc to work with us at UCSF on a project at the intersection of machine learning, computational and clinical neuroscience. The project seeks to develop quantitative methods to analyze multi-modal data from patients with neurological disease. This involves predictive modeling, interpretation inference, artificial neural networks, biomedical image and signal processing, visualization, etc. This is an opportunity to work on a highly impactful real-world problem within an interdisciplinary and collaborative environment.
Attached, you can find more information about the position.
Looking forward to hearing from you!
Reza
--
Reza Abbasi-Asl, PhD
Assistant Professor | Department of Neurology
Director of Data Analytics and Visualization | Weill Institute for Neurosciences
University of California, San Francisco
Reza.AbbasiAsl@ucsf.edu | abbasilab.org
✔️ @ApplyTime
Master R2 Internship in Natural Language Processing: weakly supervised learning for hate speech detection
Motivations and context
Recent years have seen a tremendous development of Internet and social networks. Unfortunately, the dark side of this growth is an increase in hate speech. Only a small percentage of people use the Internet for unhealthy activities such as hate speech. However, the impact of this low percentage of users is extremely damaging.
Hate speech is the subject of different national and international legal frameworks. Manual monitoring and moderating the Internet and the social media content to identify and remove hate speech is extremely expensive. This internship aims at designing methods for automatic learning of hate speech detection systems on the Internet and social media data. Despite the studies already published on this subject, the results show that the task remains very difficult (Schmidt et al., 2017; Zhang et al., 2018).
More info
✔️ @ApplyTime
Motivations and context
Recent years have seen a tremendous development of Internet and social networks. Unfortunately, the dark side of this growth is an increase in hate speech. Only a small percentage of people use the Internet for unhealthy activities such as hate speech. However, the impact of this low percentage of users is extremely damaging.
Hate speech is the subject of different national and international legal frameworks. Manual monitoring and moderating the Internet and the social media content to identify and remove hate speech is extremely expensive. This internship aims at designing methods for automatic learning of hate speech detection systems on the Internet and social media data. Despite the studies already published on this subject, the results show that the task remains very difficult (Schmidt et al., 2017; Zhang et al., 2018).
More info
✔️ @ApplyTime
team.inria.fr
Master R2 Internship in Natural Language Processing: weakly supervised learning for hate speech detection
Supervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS Team: Multispeech, LORIA-INRIA Contact: illina@loria.fr, dominique.fohr@loria.fr Duration: 5-6 months Deadline to apply : March 1th, 2020 Required skills: background in statistics, natural language processing…
PhD proposal : Detection of "novelty" in images and videos
Dear Colleagues,
Please find enclosed a proposal for a three-year funded PhD thesis.
It is a proposal from the Pattern Recognition and Image Analysis team of the computer science laboratory of Tours in France and the OTODO company.
A first deadline for evaluation of candidates will be December 15th, 2019;
Regards,
--
Romain RAVEAUX - Associate Professor (MCF HDR)
Université François Rabelais Tours,
Polytech'Tours engineering school, computer science department,
Computer science lab (LIFAT EA 6300), RFAI team, office 207
64 av. Jean Portalis, 37200 TOURS, France
Phone: +33 (0)2 47 36 14 27
LIFAT: http://www.lifat.univ-tours.fr
Polytech: www.polytech.univ-tours.fr
Personal web page : http://romain.raveaux.free.fr
✔️ @ApplyTime
Dear Colleagues,
Please find enclosed a proposal for a three-year funded PhD thesis.
It is a proposal from the Pattern Recognition and Image Analysis team of the computer science laboratory of Tours in France and the OTODO company.
A first deadline for evaluation of candidates will be December 15th, 2019;
Regards,
--
Romain RAVEAUX - Associate Professor (MCF HDR)
Université François Rabelais Tours,
Polytech'Tours engineering school, computer science department,
Computer science lab (LIFAT EA 6300), RFAI team, office 207
64 av. Jean Portalis, 37200 TOURS, France
Phone: +33 (0)2 47 36 14 27
LIFAT: http://www.lifat.univ-tours.fr
Polytech: www.polytech.univ-tours.fr
Personal web page : http://romain.raveaux.free.fr
✔️ @ApplyTime
Laboratoire d Informatique
LIFAT - Home
Postdoctoral positions are available in the computational neuroscience laboratory of Dr. Mark Goldman at the University of California at Davis for work on two multi-investigator projects seeking to understand multi-brain region mechanisms of neural integration, working memory, and motor control, as described below. Dr. Goldman will be at the 2019 Society for Neuroscience meeting for those interested in discussing the projects more in person.
Project denoscriptions:
1) Modeling the cellular resolution, brain-wide mechanisms of decision-making. This project involves studying the neural circuit dynamics underlying working memory during "accumulation-of-evidence" tasks in rodents. Our goal is to understand working memory at a brain-wide, integrative level. Brain regions being investigated include frontal, parietal, and sensory cortices, striatum, hippocampus, and cerebellum, with a view to achieving a whole picture of how these different brain structures work together dynamically to process, store, and transfer information to one another.
The project is a collaborative effort with the research groups of David Tank (dwtank@princeton.edu), Carlos Brody (brody@princeton.edu), Jonathan Pillow (jpillow@princeton.edu), Sebastian Seung (sseung@princeton.edu), Sam Wang (sswang@princeton.edu), and Ilana Witten (iwitten@princeton.edu). The modeling postdoc will work collaboratively with the Princeton University research team through remote and in-person visits to Princeton.
For more information, see our website here: www.braincogs.org.
2) Cerebellum-mediated plasticity of neural integrator dynamics. The mathematical accumulation (integration) of signals in a short-term memory buffer is a core computation in cognitive and motor circuits. However, the circuit dynamics underlying this computation, and its tuning by plasticity mechanisms, is currently unknown. This project uses the integration of eye velocity commands that control the position of the eyes as a model system to understanding the circuit dynamics and tuning of neural integrators. Data at an unprecedented level of resolution will be provided by cellular resolution calcium imaging, perturbations, and connectomic reconstruction of oculomotor circuits in the larval zebrafish, and genetically targeted recordings and perturbations of activity in the mouse. Theory and computational analysis will be used to dissect the circuit mechanisms, sites of plasticity, and broader computational principles underlying oculomotor learning.
The project is a collaborative effort with the research groups of Dr. Emre Aksay (Weill Medical College of Cornell University, ema2004@med.cornell.edu), Dr. Sebastian Seung (Princeton University, sseung@princeton.edu), and Dr. Jennifer Raymond (Stanford University, jenr@stanford.edu).
UC Davis, the #5 US public university (Wall Street Journal/Times Higher Education College Ranking) is located 1 hour from the San Francisco Bay Area. Candidates will be part of the recently expanded UC Davis computational neuroscience (5 theory faculty) and decision-making (5 core faculty) communities, bolstered by the Initiative in Computational Sciences (http://comphip2017.ucdavis.edu/). Candidates are expected to have training in a quantitatively rigorous discipline such as math, physics, engineering, computer science, statistics, or computational neuroscience. Interested candidates should send a CV, brief statement of previous research and future research interests, and contact information for 3 references to: Mark Goldman, msgoldman@ucdavis.edu.
✔️ @ApplyTime
Project denoscriptions:
1) Modeling the cellular resolution, brain-wide mechanisms of decision-making. This project involves studying the neural circuit dynamics underlying working memory during "accumulation-of-evidence" tasks in rodents. Our goal is to understand working memory at a brain-wide, integrative level. Brain regions being investigated include frontal, parietal, and sensory cortices, striatum, hippocampus, and cerebellum, with a view to achieving a whole picture of how these different brain structures work together dynamically to process, store, and transfer information to one another.
The project is a collaborative effort with the research groups of David Tank (dwtank@princeton.edu), Carlos Brody (brody@princeton.edu), Jonathan Pillow (jpillow@princeton.edu), Sebastian Seung (sseung@princeton.edu), Sam Wang (sswang@princeton.edu), and Ilana Witten (iwitten@princeton.edu). The modeling postdoc will work collaboratively with the Princeton University research team through remote and in-person visits to Princeton.
For more information, see our website here: www.braincogs.org.
2) Cerebellum-mediated plasticity of neural integrator dynamics. The mathematical accumulation (integration) of signals in a short-term memory buffer is a core computation in cognitive and motor circuits. However, the circuit dynamics underlying this computation, and its tuning by plasticity mechanisms, is currently unknown. This project uses the integration of eye velocity commands that control the position of the eyes as a model system to understanding the circuit dynamics and tuning of neural integrators. Data at an unprecedented level of resolution will be provided by cellular resolution calcium imaging, perturbations, and connectomic reconstruction of oculomotor circuits in the larval zebrafish, and genetically targeted recordings and perturbations of activity in the mouse. Theory and computational analysis will be used to dissect the circuit mechanisms, sites of plasticity, and broader computational principles underlying oculomotor learning.
The project is a collaborative effort with the research groups of Dr. Emre Aksay (Weill Medical College of Cornell University, ema2004@med.cornell.edu), Dr. Sebastian Seung (Princeton University, sseung@princeton.edu), and Dr. Jennifer Raymond (Stanford University, jenr@stanford.edu).
UC Davis, the #5 US public university (Wall Street Journal/Times Higher Education College Ranking) is located 1 hour from the San Francisco Bay Area. Candidates will be part of the recently expanded UC Davis computational neuroscience (5 theory faculty) and decision-making (5 core faculty) communities, bolstered by the Initiative in Computational Sciences (http://comphip2017.ucdavis.edu/). Candidates are expected to have training in a quantitatively rigorous discipline such as math, physics, engineering, computer science, statistics, or computational neuroscience. Interested candidates should send a CV, brief statement of previous research and future research interests, and contact information for 3 references to: Mark Goldman, msgoldman@ucdavis.edu.
✔️ @ApplyTime
INTERDISCIPLINARY HIRING IN DATA SCIENCE, NETWORK SCIENCE, & COMPUTATIONAL NEUROSCIENCE
UC Davis is dramatically expanding in the interrelated areas of data science, network science, and computational neuroscience. We have been approved to hire 10+ new faculty across a range of...
PostDoc in Bioinformatics / Computational Biology (Luxembourg)
Fixed-term contract 2 years (extension possible), 40h/week, competitive salary
Ref. BIOINF-ML-POSTDOC
Start date: as soon as possible
Denoscription
We seek a highly motivated bioinformatician who is experienced in the analysis of large-scale biomedical omics data, using statistical methods and machine learning, and bioscientific data processing and programming. The candidate will conduct integrative stratification analyses of biomedical data, focusing on molecular, clinical and neuroimaging data for neurodegenerative diseases. This will include the review, set-up and application of software analysis pipelines, and the joint interpretation of disease-related data together with experimental and clinical collaborators. The project will use new biological high-throughput data from patients, healthy controls, as well as in-vitro and in-vivo disease models. With the help of pathway-, network- and machine learning analyses, the goal is to improve the mechanistic understanding of molecular and cellular perturbations in common neurological disorders.
Your Profile
The candidate will have a PhD or equivalent degree in bioinformatics or computational biology
Prior experience in large-scale data processing and statistics / machine learning is required
A track record of previous publications in bioinformatics analysis of large-scale biological data (e.g. omics, neuroimaging data) should be outlined in the CV
Demonstrated skills and knowledge in next-generation sequencing data analysis, biostatistics, machine learning, pathway and network analysis are highly advantageous
The candidate should have a cross-disciplinary aptitude, strong organizational and interpersonal skills, and a keen interest in collaborative biomedical research
Fluency in oral and written English
We offer
A fully funded position with a highly competitive salary.
An opportunity to join the Luxembourg Centre of Systems Biomedicine with an international and interdisciplinary ethos.
Working in a scientifically stimulating, innovative, dynamic, well- equipped, and international surrounding.
Opportunity to work closely with international academic partners.
State-of-the-art research facilities and computational equipment
Further Information
Applications should contain the following documents (ideally combined into one pdf document):
A detailed Curriculum vitae
A motivation letter, including a brief denoscription of past research experience and future interests, as well as the earliest possible starting date
Copies of degree certificates and trannoscripts
Name and contact details of at least two referees
For further information, please contact:
Enrico Glaab
enrico.glaab (at) uni.lu
✔️ @ApplyTime
Fixed-term contract 2 years (extension possible), 40h/week, competitive salary
Ref. BIOINF-ML-POSTDOC
Start date: as soon as possible
Denoscription
We seek a highly motivated bioinformatician who is experienced in the analysis of large-scale biomedical omics data, using statistical methods and machine learning, and bioscientific data processing and programming. The candidate will conduct integrative stratification analyses of biomedical data, focusing on molecular, clinical and neuroimaging data for neurodegenerative diseases. This will include the review, set-up and application of software analysis pipelines, and the joint interpretation of disease-related data together with experimental and clinical collaborators. The project will use new biological high-throughput data from patients, healthy controls, as well as in-vitro and in-vivo disease models. With the help of pathway-, network- and machine learning analyses, the goal is to improve the mechanistic understanding of molecular and cellular perturbations in common neurological disorders.
Your Profile
The candidate will have a PhD or equivalent degree in bioinformatics or computational biology
Prior experience in large-scale data processing and statistics / machine learning is required
A track record of previous publications in bioinformatics analysis of large-scale biological data (e.g. omics, neuroimaging data) should be outlined in the CV
Demonstrated skills and knowledge in next-generation sequencing data analysis, biostatistics, machine learning, pathway and network analysis are highly advantageous
The candidate should have a cross-disciplinary aptitude, strong organizational and interpersonal skills, and a keen interest in collaborative biomedical research
Fluency in oral and written English
We offer
A fully funded position with a highly competitive salary.
An opportunity to join the Luxembourg Centre of Systems Biomedicine with an international and interdisciplinary ethos.
Working in a scientifically stimulating, innovative, dynamic, well- equipped, and international surrounding.
Opportunity to work closely with international academic partners.
State-of-the-art research facilities and computational equipment
Further Information
Applications should contain the following documents (ideally combined into one pdf document):
A detailed Curriculum vitae
A motivation letter, including a brief denoscription of past research experience and future interests, as well as the earliest possible starting date
Copies of degree certificates and trannoscripts
Name and contact details of at least two referees
For further information, please contact:
Enrico Glaab
enrico.glaab (at) uni.lu
✔️ @ApplyTime
Post-Doc Opportunity at the Brain and Creativity Institute at the University of Southern California
On behalf of Assal Habibi
Dear Colleagues,
We have a post-doc opportunity at the Brain and Creativity Institute at the University of Southern California. We are looking for a candidate to work on our ongoing projects on music training and child development. Primary responsibilities include data collection from pediatric population (neuroimaging and psychometric measures), managing and analyzing large scale MRI (including Functional MRI, Diffusion Tensor Imaging and Structural MRI) data and publication of findings. The position is funded for two years.
Please see the ad details here:
https://usccareers.usc.edu/job/los-angeles/postdoctoral-scholar-research-associate/1209/13424006
Please contact me directly with any questions: ahabibi@usc.edu
✔️ @ApplyTime
On behalf of Assal Habibi
Dear Colleagues,
We have a post-doc opportunity at the Brain and Creativity Institute at the University of Southern California. We are looking for a candidate to work on our ongoing projects on music training and child development. Primary responsibilities include data collection from pediatric population (neuroimaging and psychometric measures), managing and analyzing large scale MRI (including Functional MRI, Diffusion Tensor Imaging and Structural MRI) data and publication of findings. The position is funded for two years.
Please see the ad details here:
https://usccareers.usc.edu/job/los-angeles/postdoctoral-scholar-research-associate/1209/13424006
Please contact me directly with any questions: ahabibi@usc.edu
✔️ @ApplyTime
usccareers.usc.edu
Postdoctoral Scholar - Research Associate at USC
Learn more about applying for Postdoctoral Scholar - Research Associate at USC
Research Fellow in Deep Reinforcement Learning for Machine Theory of Mind @ Oxford Brookes
The Visual Artificial Intelligence Laboratory at Oxford Brookes University, led by Professor Fabio Cuzzolin, is seeking a Research Fellow in Deep Reinforcement Learning for Machine Theory of Mind, to be appointed asap, on a 30-month contract.
The deadline for application is 16th December 2019.
Salary: £31,866 rising annually to £34,804.
The Research Fellow will lead the Lab’s efforts within the Leverhulme Trust-funded Research Project Grant “Theory of mind at the interface of neuroscience and AI”, in partnership with Prof Barbara Sahakian of the Department of Psychiatry of the University of Cambridge, a leading figure in the field of Clinical Neuropsychology and the President of the International Neuroethics Society (https://www.bcni.psychol.cam.ac.uk/directory/barbara-sahakian). The project aims to provide machines with ‘theory of mind’ capabilities, i.e., the ability to guess the reasoning processes and intentions of other intelligent agents. It will explore the possibility of creating flexible theory of mind simulations based on the concept of reconfigurable deep networks, trained in a reinforcement learning setting, as well as refine and test new cognitive models for these functionalities in the human brain. Similarities and differences with inverse reinforcement learning will also be investigated.
The Visual Artificial Intelligence Laboratory (http://cms.brookes.ac.uk/staff/FabioCuzzolin/) is of the top research groups in the world in deep learning for action detection, and is heavily investing in the topic of predicting future behaviour and events. The team designed in 2017 the first system yet able to localise multiple actions on the image plane in real time, and our algorithms regularly compete for the best detection accuracies. Former PhD students have moved on to postdoc positions at Oxford, Stanford and ETH Zurich.
The Laboratory is currently running on a budget of almost £2 million, with seven live funded projects spanning computer vision, machine learning, general AI, autonomous driving, surgical and mobile robotics, AI for healthcare, decision making and uncertainty theory. We are also significantly involved in the new £1.9 Oxford Brookes AI Incubator (AIDA), and in the University’s Ethical AI Institute.
For an overview of our research themes and projects, please consult
http://cms.brookes.ac.uk/staff/FabioCuzzolin/files/VAIL.pdf
You will join a vibrant and fast growing team projected to comprise 25+ people in 2019-20. You will be working on a network of cutting edge 4-GPU and 8-GPU workstations. The Lab collaborates with various companies including Huawei, Leonardo, Cisco, Ocado, Cortexica and Oxbotica, and is in the process of spinning off a company in the area of action detection. We closely work with Cambridge University’s Neuroscience, as well as Oxford University’s Engineering and Computer Science Departments, and have established links with IIT Bombay, Harvard, Seoul National, the Fraunhofer Institute, and numerous other European partners.
You are encouraged to contact Prof Cuzzolin at fabio.cuzzolin@brookes.ac.uk for more information and informal feedback on your application.
To formally apply, please follow the instructions provided here:
https://www.jobs.ac.uk/job/BWZ564/research-fellow-in-reinforcement-learning-for-machine-theory-of-mind
✔️ @ApplyTime
The Visual Artificial Intelligence Laboratory at Oxford Brookes University, led by Professor Fabio Cuzzolin, is seeking a Research Fellow in Deep Reinforcement Learning for Machine Theory of Mind, to be appointed asap, on a 30-month contract.
The deadline for application is 16th December 2019.
Salary: £31,866 rising annually to £34,804.
The Research Fellow will lead the Lab’s efforts within the Leverhulme Trust-funded Research Project Grant “Theory of mind at the interface of neuroscience and AI”, in partnership with Prof Barbara Sahakian of the Department of Psychiatry of the University of Cambridge, a leading figure in the field of Clinical Neuropsychology and the President of the International Neuroethics Society (https://www.bcni.psychol.cam.ac.uk/directory/barbara-sahakian). The project aims to provide machines with ‘theory of mind’ capabilities, i.e., the ability to guess the reasoning processes and intentions of other intelligent agents. It will explore the possibility of creating flexible theory of mind simulations based on the concept of reconfigurable deep networks, trained in a reinforcement learning setting, as well as refine and test new cognitive models for these functionalities in the human brain. Similarities and differences with inverse reinforcement learning will also be investigated.
The Visual Artificial Intelligence Laboratory (http://cms.brookes.ac.uk/staff/FabioCuzzolin/) is of the top research groups in the world in deep learning for action detection, and is heavily investing in the topic of predicting future behaviour and events. The team designed in 2017 the first system yet able to localise multiple actions on the image plane in real time, and our algorithms regularly compete for the best detection accuracies. Former PhD students have moved on to postdoc positions at Oxford, Stanford and ETH Zurich.
The Laboratory is currently running on a budget of almost £2 million, with seven live funded projects spanning computer vision, machine learning, general AI, autonomous driving, surgical and mobile robotics, AI for healthcare, decision making and uncertainty theory. We are also significantly involved in the new £1.9 Oxford Brookes AI Incubator (AIDA), and in the University’s Ethical AI Institute.
For an overview of our research themes and projects, please consult
http://cms.brookes.ac.uk/staff/FabioCuzzolin/files/VAIL.pdf
You will join a vibrant and fast growing team projected to comprise 25+ people in 2019-20. You will be working on a network of cutting edge 4-GPU and 8-GPU workstations. The Lab collaborates with various companies including Huawei, Leonardo, Cisco, Ocado, Cortexica and Oxbotica, and is in the process of spinning off a company in the area of action detection. We closely work with Cambridge University’s Neuroscience, as well as Oxford University’s Engineering and Computer Science Departments, and have established links with IIT Bombay, Harvard, Seoul National, the Fraunhofer Institute, and numerous other European partners.
You are encouraged to contact Prof Cuzzolin at fabio.cuzzolin@brookes.ac.uk for more information and informal feedback on your application.
To formally apply, please follow the instructions provided here:
https://www.jobs.ac.uk/job/BWZ564/research-fellow-in-reinforcement-learning-for-machine-theory-of-mind
✔️ @ApplyTime
Open Postdoc position at Inria SequeL, Lille (France)
Hello,
We are opening a postdoc position at Inria SequeL (France), under super-vision by OA Maillard. The topic is on adressing novel challenges in Reinforcement Learning theory. These challenges are inspired by an application of sequential decision making to biodiversity-preserving agriculture. Please contact O-A. Maillard with 3 of your main publications, CV, motivation letter and recommendation lette before March 2019.
https://team.inria.fr/sequel/
Best,
Odalric-ambrym Maillard
✔️ @ApplyTime
Hello,
We are opening a postdoc position at Inria SequeL (France), under super-vision by OA Maillard. The topic is on adressing novel challenges in Reinforcement Learning theory. These challenges are inspired by an application of sequential decision making to biodiversity-preserving agriculture. Please contact O-A. Maillard with 3 of your main publications, CV, motivation letter and recommendation lette before March 2019.
https://team.inria.fr/sequel/
Best,
Odalric-ambrym Maillard
✔️ @ApplyTime
سلام وقت عالی بخیر
ببخشید استاد من دنبال یک دانشجوی با انگیزه و مستعد درمقطع دکتری می باشد که این پوزیشن full fund می باشد.حوزه این پوزیشن مدیریت انرژی است و رشته های فیزیک-برق-ریاضی-اقتصاد می توانند اپلای کنند.
ممنون می شوم این پوزیشن را در سایت خود قرار دهید.
با احترام و تشکر
اینک پوزیشن 👇👇
https://www.findaphd.com/phds/project/a-game-theoretic-energy-management-for-home-micro-grids-gtem4hm-considering-renewable-resources-and-electrical-vehicles-advert-reference-rdf20-ee-mpee-marzband/?p116089
✔️ @ApplyTime
ببخشید استاد من دنبال یک دانشجوی با انگیزه و مستعد درمقطع دکتری می باشد که این پوزیشن full fund می باشد.حوزه این پوزیشن مدیریت انرژی است و رشته های فیزیک-برق-ریاضی-اقتصاد می توانند اپلای کنند.
ممنون می شوم این پوزیشن را در سایت خود قرار دهید.
با احترام و تشکر
اینک پوزیشن 👇👇
https://www.findaphd.com/phds/project/a-game-theoretic-energy-management-for-home-micro-grids-gtem4hm-considering-renewable-resources-and-electrical-vehicles-advert-reference-rdf20-ee-mpee-marzband/?p116089
✔️ @ApplyTime
New Post-doc Opening at U. of Toronto on Deep Learning / RL for Traffic Prediction and Control
Salary for the position is highly competitive. An initial offer would be made for one year with possibility of renewal for one or more years. Post-docs are expected to publish in the top venues relevant to the project research area and to produce high-quality deliverable code for use by research funding partners.
If you are interested, please email ssanner@mie.utoronto.ca with the following:
(a) your CV (clearly listing all publications),
(b) your github, gitlab, or bitbucket public account link with deep learning / reinforcement learning coding projects that we can browse,
(c) 1-2 sentences in your email stating why the position interests you and why you think you are appropriate for the position.
We are seeking to fill this new opening as soon as possible.
Dr. Scott P. Sanner
Assistant Professor, Industrial Engineering
Cross-appointed, Computer Science
Faculty Affiliate, Vector Institute
University of Toronto, Toronto, ON, Canada
Email: ssanner@mie.utoronto.ca
Website: http://d3m.mie.utoronto.ca
✔️ @ApplyTime
Salary for the position is highly competitive. An initial offer would be made for one year with possibility of renewal for one or more years. Post-docs are expected to publish in the top venues relevant to the project research area and to produce high-quality deliverable code for use by research funding partners.
If you are interested, please email ssanner@mie.utoronto.ca with the following:
(a) your CV (clearly listing all publications),
(b) your github, gitlab, or bitbucket public account link with deep learning / reinforcement learning coding projects that we can browse,
(c) 1-2 sentences in your email stating why the position interests you and why you think you are appropriate for the position.
We are seeking to fill this new opening as soon as possible.
Dr. Scott P. Sanner
Assistant Professor, Industrial Engineering
Cross-appointed, Computer Science
Faculty Affiliate, Vector Institute
University of Toronto, Toronto, ON, Canada
Email: ssanner@mie.utoronto.ca
Website: http://d3m.mie.utoronto.ca
✔️ @ApplyTime
Seeking PostDoctoral Fellow in Machine Learning (Survival Prediction; Medical Informatics)
Applications are invited for a PostDoctoral Fellow position to study issues related to Survival Prediction / Analysis, in the Greiner Lab -- in association with the University of Alberta (Edmonton), and Amii (the Alberta Machine Intelligence Institute). The successful candidate should have a PhD in computing science or a related area, with a focus on machine learning and statistics, and an interest in medical applications, and be able to ...
design and implement novel algorithms
conduct and analyze meaningful empirical studies
prove relevant theoretical results
communicate effectively, especially with people outside of machine learning.
(Note a background in Medical Informatics is a plus!)
If you are interested, check out https://tinyurl.com/PDF-Amii-Survival
Note: I will be at NeurIPS next week (9-14/Dec) -- contact me if you want to meet to discuss this position.
✔️ @ApplyTime
Applications are invited for a PostDoctoral Fellow position to study issues related to Survival Prediction / Analysis, in the Greiner Lab -- in association with the University of Alberta (Edmonton), and Amii (the Alberta Machine Intelligence Institute). The successful candidate should have a PhD in computing science or a related area, with a focus on machine learning and statistics, and an interest in medical applications, and be able to ...
design and implement novel algorithms
conduct and analyze meaningful empirical studies
prove relevant theoretical results
communicate effectively, especially with people outside of machine learning.
(Note a background in Medical Informatics is a plus!)
If you are interested, check out https://tinyurl.com/PDF-Amii-Survival
Note: I will be at NeurIPS next week (9-14/Dec) -- contact me if you want to meet to discuss this position.
✔️ @ApplyTime
Google Docs
Seeking PostDoc - Survival Prediction 2019
Post-Doctoral Fellow Machine Learning (Survival Prediction) We are looking for a talented, knowledgeable and ambitious candidate for a 2-year post-doctoral fellow (PDF) position in machine learning, to study Survival Prediction and Analysis, within the…
2 full-time academic position vacancies on Data Science and related topics in ULB, Brussels, Belgium
2 full-time academic position vacancies on Data Science and related topics in ULB, Brussels, Belgium !
All details in:
Faculty of. Sciences:
http://wwwdev.ulb.ac.be/greffe/files/6544.pdf
Faculty of Applied Sciences:
http://wwwdev.ulb.ac.be/greffe/files/6551.pdf
—-----------------------
Pr. Gianluca Bontempi
co-Head of the Machine Learning Group
Département d'Informatique
Université Libre de Bruxelles
✔️ @ApplyTime
2 full-time academic position vacancies on Data Science and related topics in ULB, Brussels, Belgium !
All details in:
Faculty of. Sciences:
http://wwwdev.ulb.ac.be/greffe/files/6544.pdf
Faculty of Applied Sciences:
http://wwwdev.ulb.ac.be/greffe/files/6551.pdf
—-----------------------
Pr. Gianluca Bontempi
co-Head of the Machine Learning Group
Département d'Informatique
Université Libre de Bruxelles
✔️ @ApplyTime
Postdoc at Monash University (Melbourne) for probabilistic & deep learning
An exciting opportunity has opened up within the Faculty of Information Technology for an exceptional Research Fellow to conduct research and develop academic research software for Machine Learning in collaboration with world-class academics in the Machine Learning Group at Monash, under the direction of Prof. Wray Buntine. The aim is to continue development of some of our research artifacts and continue additional related research.
The job advertisement is here:
http://careers.pageuppeople.com/513/cw/en/job/601042/research-fellow
Basic details:
Melbourne is rated as one of the world's most livable cities.
Monash's ML group is very strong with excellent international funding and world reknown researchers.
Exciting innovative research with probabilistic and/or deep learning approaches.
For questions or applications, follow the link.
✔️ @ApplyTime
An exciting opportunity has opened up within the Faculty of Information Technology for an exceptional Research Fellow to conduct research and develop academic research software for Machine Learning in collaboration with world-class academics in the Machine Learning Group at Monash, under the direction of Prof. Wray Buntine. The aim is to continue development of some of our research artifacts and continue additional related research.
The job advertisement is here:
http://careers.pageuppeople.com/513/cw/en/job/601042/research-fellow
Basic details:
Melbourne is rated as one of the world's most livable cities.
Monash's ML group is very strong with excellent international funding and world reknown researchers.
Exciting innovative research with probabilistic and/or deep learning approaches.
For questions or applications, follow the link.
✔️ @ApplyTime
Postdoc position for recently graduated PhD student
Dear all,
we have a postdoc fellowship available at the German Aerospace Center near Munich, Germany (for one year, with the
possibility of extension to a second year) for recent PhD graduates. This position is focused on the development of machine learning algorithms to analyze particle trajectories, both from our experiment on "complex plasmas" on board the International Space Station, and from simulations. Prior experience in machine learning is advantageous, but not required, provided the applicant can demonstrate strong programming skills and the capability to acquire missing skills.
Here is the announcement of the fellowship:
https://www.daad.de/medien/deutschland/stipendien/formulare/400_postdoc_mp_op_particle_trajectories_postdoc_schwabe_2019.pdf
And this is some general information of the program:
https://www.daad.de/deutschland/stipendium/datenbank/en/21148-scholarship-database/?origin=4&status=4&subjectGrps=&daad=&q=&page=1&detail=50019749
Best regards,
Mierk Schwabe
✔️ @ApplyTime
Dear all,
we have a postdoc fellowship available at the German Aerospace Center near Munich, Germany (for one year, with the
possibility of extension to a second year) for recent PhD graduates. This position is focused on the development of machine learning algorithms to analyze particle trajectories, both from our experiment on "complex plasmas" on board the International Space Station, and from simulations. Prior experience in machine learning is advantageous, but not required, provided the applicant can demonstrate strong programming skills and the capability to acquire missing skills.
Here is the announcement of the fellowship:
https://www.daad.de/medien/deutschland/stipendien/formulare/400_postdoc_mp_op_particle_trajectories_postdoc_schwabe_2019.pdf
And this is some general information of the program:
https://www.daad.de/deutschland/stipendium/datenbank/en/21148-scholarship-database/?origin=4&status=4&subjectGrps=&daad=&q=&page=1&detail=50019749
Best regards,
Mierk Schwabe
✔️ @ApplyTime
PhD project in Prof Hugh Piggins lab (Bristol)
We are looking for a motivated student for a systems neuroscience/physiology PhD project 'Combatting Brain and Body Ageing with Exercise'. The project is funded via the BBSRC SWBIO program and involves Profs. Piggins and Pickering (Bristol) and Drs. Ellacott and Walker (Exeter). The deadline is December the 2nd. Please see the link below for details.
https://cpb-eu-w2.wpmucdn.com/blogs.bristol.ac.uk/dist/f/373/files/2019/10/swbio-20-project-74.pdf
(posted on behalf of Prof Hugh Piggins, University of Bristol)
Prof Mark Humphries | MRC Senior non-Clinical Research Fellow | Chair in Computational Neuroscience
@markdhumphries
Public blog: medium.com/the-spike
✔️ @ApplyTime
We are looking for a motivated student for a systems neuroscience/physiology PhD project 'Combatting Brain and Body Ageing with Exercise'. The project is funded via the BBSRC SWBIO program and involves Profs. Piggins and Pickering (Bristol) and Drs. Ellacott and Walker (Exeter). The deadline is December the 2nd. Please see the link below for details.
https://cpb-eu-w2.wpmucdn.com/blogs.bristol.ac.uk/dist/f/373/files/2019/10/swbio-20-project-74.pdf
(posted on behalf of Prof Hugh Piggins, University of Bristol)
Prof Mark Humphries | MRC Senior non-Clinical Research Fellow | Chair in Computational Neuroscience
@markdhumphries
Public blog: medium.com/the-spike
✔️ @ApplyTime
#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 است! جدا از پستهای دیگری که در برگیرندهی فایلهای آموزشی یا راهنماییهایی جهت طی مسیر اپلای بودند. و سوا از ارائهی مشاورهی رایگان به بیش از 3000 نفر فقط از طریق تلگرام!!!
چه بسیار دوستانی که برای آن پوزیشنها اپلای کردند، و چه بسیار کسانی که در نهایت پاسخ مثبت دریافت کردهاند. مبارکشان باشد! :)
در ادامهی روند تصمیم گرفتیم که با ایجاد یک گروه تلگرامی برای کانال پوزیشنها، مشارکت عزیزانی که علاقهمند هستند به دیگران کمک کنند را نیز جلب کنیم. بسیاری از ما احتمالا در طول روز با موقعیتها یا بورسیههای تحصیلیای برخورد میکنیم که برخی از آنها برای خود ما مناسب نیست، ولی شاید برای دیگر هموطنانمان مفید باشد. در نتیجه چه خوب است که لینک آنها را با دیگران به اشتراک بگذاریم! اگر اپلای تایم به تنهای توانسته بیش از 5000 پوزیشن را به اشتراک بگذارد، چنانچه همهی ما با هم همکاری کنیم، چند پوزیشن میشود به اشتراک گذاشت؟!؟
شما میتوانید با مراجعه به گروه زیر، لینک این گونه موقعیتها یا بورسیههای تحصیلی را با دیگران به اشتراک بگذارید:
https://news.1rj.ru/str/applytime_academic_positions
قوانین مربوط به اشتراک گذاری لینکها در خود گروه توضیح داده شده است. اما به اختصار لازم به توضیح است، که این گروه برای تبادل نظر در زمینهی اپلای طراحی نشده است، بلکه تنها و تنها جهت به اشتراک گذاری موقعیتها یا بورسیههای تحصیلی میباشد.
اپلای تایم نیز مانند گذشته پوزیشنهایی را از طریق کانال یا وبسایت به اشتراک خواهد گذاشت.
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 است! جدا از پستهای دیگری که در برگیرندهی فایلهای آموزشی یا راهنماییهایی جهت طی مسیر اپلای بودند. و سوا از ارائهی مشاورهی رایگان به بیش از 3000 نفر فقط از طریق تلگرام!!!
چه بسیار دوستانی که برای آن پوزیشنها اپلای کردند، و چه بسیار کسانی که در نهایت پاسخ مثبت دریافت کردهاند. مبارکشان باشد! :)
در ادامهی روند تصمیم گرفتیم که با ایجاد یک گروه تلگرامی برای کانال پوزیشنها، مشارکت عزیزانی که علاقهمند هستند به دیگران کمک کنند را نیز جلب کنیم. بسیاری از ما احتمالا در طول روز با موقعیتها یا بورسیههای تحصیلیای برخورد میکنیم که برخی از آنها برای خود ما مناسب نیست، ولی شاید برای دیگر هموطنانمان مفید باشد. در نتیجه چه خوب است که لینک آنها را با دیگران به اشتراک بگذاریم! اگر اپلای تایم به تنهای توانسته بیش از 5000 پوزیشن را به اشتراک بگذارد، چنانچه همهی ما با هم همکاری کنیم، چند پوزیشن میشود به اشتراک گذاشت؟!؟
شما میتوانید با مراجعه به گروه زیر، لینک این گونه موقعیتها یا بورسیههای تحصیلی را با دیگران به اشتراک بگذارید:
https://news.1rj.ru/str/applytime_academic_positions
قوانین مربوط به اشتراک گذاری لینکها در خود گروه توضیح داده شده است. اما به اختصار لازم به توضیح است، که این گروه برای تبادل نظر در زمینهی اپلای طراحی نشده است، بلکه تنها و تنها جهت به اشتراک گذاری موقعیتها یا بورسیههای تحصیلی میباشد.
اپلای تایم نیز مانند گذشته پوزیشنهایی را از طریق کانال یا وبسایت به اشتراک خواهد گذاشت.
New Post-doc Opening at U. of Toronto on Deep Learning / RL for Traffic Prediction and Control
Salary for the position is highly competitive. An initial offer would be made for one year with possibility of renewal for one or more years. Post-docs are expected to publish in the top venues relevant to the project research area and to produce high-quality deliverable code for use by research funding partners.
If you are interested, please email ssanner@mie.utoronto.ca with the following:
(a) your CV (clearly listing all publications),
(b) your github, gitlab, or bitbucket public account link with deep learning / reinforcement learning coding projects that we can browse,
(c) 1-2 sentences in your email stating why the position interests you and why you think you are appropriate for the position.
We are seeking to fill this new opening as soon as possible.
Dr. Scott P. Sanner
Assistant Professor, Industrial Engineering
Cross-appointed, Computer Science
Faculty Affiliate, Vector Institute
University of Toronto, Toronto, ON, Canada
Email: ssanner@mie.utoronto.ca
Website: http://d3m.mie.utoronto.ca
✔️ @ApplyTime
Salary for the position is highly competitive. An initial offer would be made for one year with possibility of renewal for one or more years. Post-docs are expected to publish in the top venues relevant to the project research area and to produce high-quality deliverable code for use by research funding partners.
If you are interested, please email ssanner@mie.utoronto.ca with the following:
(a) your CV (clearly listing all publications),
(b) your github, gitlab, or bitbucket public account link with deep learning / reinforcement learning coding projects that we can browse,
(c) 1-2 sentences in your email stating why the position interests you and why you think you are appropriate for the position.
We are seeking to fill this new opening as soon as possible.
Dr. Scott P. Sanner
Assistant Professor, Industrial Engineering
Cross-appointed, Computer Science
Faculty Affiliate, Vector Institute
University of Toronto, Toronto, ON, Canada
Email: ssanner@mie.utoronto.ca
Website: http://d3m.mie.utoronto.ca
✔️ @ApplyTime
MERL is seeking a motivated and qualified individual to conduct research in safe reinforcement learning (RL) and deep learning algorithms for robotics applications. The ideal candidate should have solid background in RL (PhD level). Knowledge of deep learning algorithms is a plus. Publication of the results produced during the internship is anticipated. Duration of the internship is expected to be 3 months. Start date is flexible.
Contact: Dr. M. Benosman
Link: https://www.merl.com/internship/openings.php?tags=benosman
✔️ @ApplyTime
Contact: Dr. M. Benosman
Link: https://www.merl.com/internship/openings.php?tags=benosman
✔️ @ApplyTime
Merl
Internship Openings
Mitsubishi Electric Research Laboratories (MERL) - Internship Openings
Hi, everyone. We're now accepting applications for summer 2020 Research Interns at the following link: https://smrtr.io/3D3zr. See my previous email below for information on our group, located in the AI Lab of UT Austin.
For the Research Scientist and Research Engineer positions linked to below, we expect to only consider applications submitted by the end of this week.
Best regards,
Brad
____________________
W. Bradley Knox, PhD
✔️ @ApplyTime
For the Research Scientist and Research Engineer positions linked to below, we expect to only consider applications submitted by the end of this week.
Best regards,
Brad
____________________
W. Bradley Knox, PhD
✔️ @ApplyTime
Telegram
Academic Positions
MERL is seeking a motivated and qualified individual to conduct research in safe reinforcement learning (RL) and deep learning algorithms for robotics applications. The ideal candidate should have solid background in RL (PhD level). Knowledge of deep learning…
7 Postdoctoral Research Positions - ML & AI for Healthcare
Computational Health Informatics (CHI) Laboratory,
Department of Engineering Science, University of Oxford
The CHI Lab, led by David Clifton (Oxford Professor of Clinical Machine Learning), has sites in Oxford (funded by the Wellcome Trust, UK government, and NHS) and Suzhou, China (funded by the Chinese government), with over 30 researchers. We develop (non-imaging) interventions for use in healthcare that are based on machine learning, typically involving time-series analysis, Bayesian nonparametrics, and generative deep learning. In close collaboration with Oxford University Hospitals, we have acquired some of the world’s largest sets of patient data (> 1M admissions). We collaborate closely with industry to deploy our methods across hospitals at scale - our work is typically patented by Oxford University Innovation, for immediate translation into healthcare practice. Recent outcomes include the public listing on the UK stock exchange of a company (now approaching 100 employees) to implement our research, alongside other spin-outs.
CHI Lab has openings for postdoctoral researcher scientists, in ML & AI for healthcare, across three sites:
· Four postdocs across a variety of novel Oxford-based clinical collaborations; https://bit.ly/387NZAI
· One postdoc in our Chinese lab in Suzhou; https://bit.ly/2OQsVYa
· Two senior postdocs in our new Wellcome Trust-funded “Flagship Centre”, based in Ho Chi Minh City, Vietnam, sited within the Oxford University Clinical Research Unit (containing 300 Oxford researchers). This initiative focuses on developing novel hospital-based AI interventions suitable for resource-constrained settings, and the posts include a generous package of “overseas allowances”. https://bit.ly/2DGQ5d4
Our lab’s members come from a variety of backgrounds, including machine learning, signal processing, imaging, statistics, and clinical medicine. You will have the opportunity to develop novel technologies based on machine learning as part of a highly collaborative team, with coinventorship on patents, publication in the engineering and medical literature (with leading clinical collaborators), and potential involvement with spin-out and industrial activity according to your interests.
Please contact Prof. David Clifton for informal enquiries: davidc@robots.ox.ac.uk
✔️ @ApplyTime
Computational Health Informatics (CHI) Laboratory,
Department of Engineering Science, University of Oxford
The CHI Lab, led by David Clifton (Oxford Professor of Clinical Machine Learning), has sites in Oxford (funded by the Wellcome Trust, UK government, and NHS) and Suzhou, China (funded by the Chinese government), with over 30 researchers. We develop (non-imaging) interventions for use in healthcare that are based on machine learning, typically involving time-series analysis, Bayesian nonparametrics, and generative deep learning. In close collaboration with Oxford University Hospitals, we have acquired some of the world’s largest sets of patient data (> 1M admissions). We collaborate closely with industry to deploy our methods across hospitals at scale - our work is typically patented by Oxford University Innovation, for immediate translation into healthcare practice. Recent outcomes include the public listing on the UK stock exchange of a company (now approaching 100 employees) to implement our research, alongside other spin-outs.
CHI Lab has openings for postdoctoral researcher scientists, in ML & AI for healthcare, across three sites:
· Four postdocs across a variety of novel Oxford-based clinical collaborations; https://bit.ly/387NZAI
· One postdoc in our Chinese lab in Suzhou; https://bit.ly/2OQsVYa
· Two senior postdocs in our new Wellcome Trust-funded “Flagship Centre”, based in Ho Chi Minh City, Vietnam, sited within the Oxford University Clinical Research Unit (containing 300 Oxford researchers). This initiative focuses on developing novel hospital-based AI interventions suitable for resource-constrained settings, and the posts include a generous package of “overseas allowances”. https://bit.ly/2DGQ5d4
Our lab’s members come from a variety of backgrounds, including machine learning, signal processing, imaging, statistics, and clinical medicine. You will have the opportunity to develop novel technologies based on machine learning as part of a highly collaborative team, with coinventorship on patents, publication in the engineering and medical literature (with leading clinical collaborators), and potential involvement with spin-out and industrial activity according to your interests.
Please contact Prof. David Clifton for informal enquiries: davidc@robots.ox.ac.uk
✔️ @ApplyTime
Fully-funded Post Doctoral Position at InterDigitl, Information Theory for Understanding and Designing Flexible Deep Neural Networks
Running deep neural networks (DNNs) at the Edge and in consumer electronic (CE) devices remains still very challenging due to limited computational and memory resources. Moreover, usually those resources are constantly varying due to other concurrent processes that may start or stop. To optimally use varying resources, one needs so-called flexible DNN models, i.e., models that can optimally exploit all resources available at a given time.
We are targeting the design of such flexible models as follows. A flexible DNN is a network containing several simplified sub-networks of different complexities. This should allow optimally exploiting all available computational resources by executing an appropriate sub-network. However, designing such networks is difficult and time consuming. More precisely, designing an architecture of a usual (non-flexible) DNN is already time consuming, since one needs trying various numbers of layers, various numbers of neurons in each layer, etc. In case of flexible models we are targeting, one needs designing architectures of all sub-networks, which drastically increases the number of tries needed.
In this work we propose to look at the DNNs under the angle of information theory. Indeed, several recent works proposed to study DNNs [1, 2, 3, 4] using the information bottleneck principle [5]. This principle allows obtaining some theoretical bounds of DNN performance by quantifying mutual information between its layers. This will allow finding some near-optimal network simplification rules (going from one sub-network to another) based on theoretical findings, thus avoiding exhaustive cumbersome design of each sub-network. Moreover, we hope that this theoretical work will lead to other new results and findings.
Qualifications
PhD, in Computer Science, Signal Processing, Machine Learning, Mathematics.
Strong analytical and problem-solving skills
Excellent Mathematical / Statistical Skills, Machine Learning and Deep Learning
Strong knowledge in Information Theory
Good programming skills and / or simulation tools. SW (C/C++/Python)
Ability to conduct independent research, propose patents and publications
Ability to conduct independent research
Excellent communication skills and ability to work in a team
ML/AI scientist with a strong expertise and relevant background in distributing model for both inference and training.
Fluent in English
Location: Rennes, France
To apply:
Please send your cv and cover letter to Alexey Ozerov Alexey.Ozerov@InterDigital.com
✔️ @ApplyTime
Running deep neural networks (DNNs) at the Edge and in consumer electronic (CE) devices remains still very challenging due to limited computational and memory resources. Moreover, usually those resources are constantly varying due to other concurrent processes that may start or stop. To optimally use varying resources, one needs so-called flexible DNN models, i.e., models that can optimally exploit all resources available at a given time.
We are targeting the design of such flexible models as follows. A flexible DNN is a network containing several simplified sub-networks of different complexities. This should allow optimally exploiting all available computational resources by executing an appropriate sub-network. However, designing such networks is difficult and time consuming. More precisely, designing an architecture of a usual (non-flexible) DNN is already time consuming, since one needs trying various numbers of layers, various numbers of neurons in each layer, etc. In case of flexible models we are targeting, one needs designing architectures of all sub-networks, which drastically increases the number of tries needed.
In this work we propose to look at the DNNs under the angle of information theory. Indeed, several recent works proposed to study DNNs [1, 2, 3, 4] using the information bottleneck principle [5]. This principle allows obtaining some theoretical bounds of DNN performance by quantifying mutual information between its layers. This will allow finding some near-optimal network simplification rules (going from one sub-network to another) based on theoretical findings, thus avoiding exhaustive cumbersome design of each sub-network. Moreover, we hope that this theoretical work will lead to other new results and findings.
Qualifications
PhD, in Computer Science, Signal Processing, Machine Learning, Mathematics.
Strong analytical and problem-solving skills
Excellent Mathematical / Statistical Skills, Machine Learning and Deep Learning
Strong knowledge in Information Theory
Good programming skills and / or simulation tools. SW (C/C++/Python)
Ability to conduct independent research, propose patents and publications
Ability to conduct independent research
Excellent communication skills and ability to work in a team
ML/AI scientist with a strong expertise and relevant background in distributing model for both inference and training.
Fluent in English
Location: Rennes, France
To apply:
Please send your cv and cover letter to Alexey Ozerov Alexey.Ozerov@InterDigital.com
✔️ @ApplyTime