Research positions in London (with option to work remotely in a first instance)
We have the following research-oriented (computer vision/AI) vacancies in a leading worldwide company. The positions are in London (UK) with the possibility (to be discussed) to work remotely in the first instance.
Please note the educational and experience requirements for each of these positions.
If interested, please contact Prof. Sergio A Velastin (sergio.velastin@ieee.org) in the first instance.
Computer Vision Researcher, Senior
Overview
The senior computer vision algorithm researcher will work to advance exploratory research and development projects in computer vision and machine learning and related fields to create highly innovative customer solutions. The researcher will develop perception & learning systems using 2D and 3D visioning systems that are Enterprise-grade and ready for real world deployment.
Responsibilities
Development of computer vision and machine learning algorithms and applications
Develop proof-of-concept implementations, prototypes and demos to fine-tune technologies to practical, state-of-the-art systems
Perform experiments and evaluations to quantify and validate improvements made to existing systems
Take responsibility for communicating with different engineers (embedded systems, backend server, computer vision) to continuously improve and optimize implemented algorithms
Create automated software tests and provide support to trouble shoot software issues when needed
Documentation of algorithms
Research fundamental problems and implement algorithmic solutions that are appropriate
Provide recommendations and solutions to problems using experience in multiple technical areas
Receive little instruction on day-to-day work and general instructions on new assignments
May influence the activities of junior level personnel
Qualifications
Masters/PhD in computer vision or machine learning; alternatively a comparable industry career, with significant experience in delivering products using state-of-the-art computer vision and/or machine learning systems
5+ years work experience
Solid foundation in computer vision; key areas of interest include object detection, tracking and recognition; multiple view geometry, 3D computer vision and SfM/SLAM; activity recognition
Scripting and prototyping languages: Python, Matlab, C/C++
Generic packages: OpenCV, Point Cloud Library
Experience with Linux, Windows and ROS platforms
Ability to grasp new concepts quickly
Ability to adapt and use these patterns in innovative ways to develop custom solutions
Ability to take initiative on issues and report results instead of waiting for task lists
Good presentation skills, both prepared and "on demand" talks
Takes ownership of their work, is self-motivated & practical in their problem-solving and thrives in a fast-paced, highly-collaborative applied research environment
Computer Vision Researcher, Senior
Overview
Build Today. Create Tomorrow. Join a team of builders, doers and problem solvers. Join Zebra.
About CTO’s Solution Incubation @ Zebra
We are an entrepreneurial team creating innovative solutions and Ai technologies for the transportation & logistics, warehouse, retail, and healthcare markets. These “first of a kind” solutions are helping retail managers improve their store operations & better serve their customers and are helping logistic managers enhance the efficiency of their operations. We focus on advanced development and application of emerging edge sensing, artificial intelligence, and workflow optimization technologies in innovative end-to-end system prototypes to solve real-world problems and advance these systems and Ai technologies into Zebra businesses.
We have the following research-oriented (computer vision/AI) vacancies in a leading worldwide company. The positions are in London (UK) with the possibility (to be discussed) to work remotely in the first instance.
Please note the educational and experience requirements for each of these positions.
If interested, please contact Prof. Sergio A Velastin (sergio.velastin@ieee.org) in the first instance.
Computer Vision Researcher, Senior
Overview
The senior computer vision algorithm researcher will work to advance exploratory research and development projects in computer vision and machine learning and related fields to create highly innovative customer solutions. The researcher will develop perception & learning systems using 2D and 3D visioning systems that are Enterprise-grade and ready for real world deployment.
Responsibilities
Development of computer vision and machine learning algorithms and applications
Develop proof-of-concept implementations, prototypes and demos to fine-tune technologies to practical, state-of-the-art systems
Perform experiments and evaluations to quantify and validate improvements made to existing systems
Take responsibility for communicating with different engineers (embedded systems, backend server, computer vision) to continuously improve and optimize implemented algorithms
Create automated software tests and provide support to trouble shoot software issues when needed
Documentation of algorithms
Research fundamental problems and implement algorithmic solutions that are appropriate
Provide recommendations and solutions to problems using experience in multiple technical areas
Receive little instruction on day-to-day work and general instructions on new assignments
May influence the activities of junior level personnel
Qualifications
Masters/PhD in computer vision or machine learning; alternatively a comparable industry career, with significant experience in delivering products using state-of-the-art computer vision and/or machine learning systems
5+ years work experience
Solid foundation in computer vision; key areas of interest include object detection, tracking and recognition; multiple view geometry, 3D computer vision and SfM/SLAM; activity recognition
Scripting and prototyping languages: Python, Matlab, C/C++
Generic packages: OpenCV, Point Cloud Library
Experience with Linux, Windows and ROS platforms
Ability to grasp new concepts quickly
Ability to adapt and use these patterns in innovative ways to develop custom solutions
Ability to take initiative on issues and report results instead of waiting for task lists
Good presentation skills, both prepared and "on demand" talks
Takes ownership of their work, is self-motivated & practical in their problem-solving and thrives in a fast-paced, highly-collaborative applied research environment
Computer Vision Researcher, Senior
Overview
Build Today. Create Tomorrow. Join a team of builders, doers and problem solvers. Join Zebra.
About CTO’s Solution Incubation @ Zebra
We are an entrepreneurial team creating innovative solutions and Ai technologies for the transportation & logistics, warehouse, retail, and healthcare markets. These “first of a kind” solutions are helping retail managers improve their store operations & better serve their customers and are helping logistic managers enhance the efficiency of their operations. We focus on advanced development and application of emerging edge sensing, artificial intelligence, and workflow optimization technologies in innovative end-to-end system prototypes to solve real-world problems and advance these systems and Ai technologies into Zebra businesses.
About the role
As a Computer Vision and Ai Researcher, you will have a unique opportunity to use your creativity both to understand the latest cutting-edge research and to tailor it for real-world applications. You will face a variety of challenges and will have access to the best hardware to do the job. We are focusing on using AI to help solve real-world problems on real-world data. This means embracing noise and complexity, both at the data level and at the methodological level. Your work will span from real-world data handling to the most advanced methods such as transfer learning, generative models, reinforcement learning, etc. with a focus on understanding quickly and experimenting even faster. You will collaborate closely both with software developers and research scientists, wielding the tools of both.
You will have experience in machine learning, either through your studies or industrial R&D projects and work to advance exploratory research and development projects in computer vision and machine learning and related fields to build highly innovative customer solutions. The algorithm researcher will develop perception & learning systems using 2D and 3D visioning systems that are Enterprise-grade and ready for real world deployment.
You want to join us because you are passionate about:
Research and applications of machine learning.
Collecting, quality checking, and analysing huge datasets.
Using and discovering the best tools to build practical AI models.
Staying up-to-date on the scientific literature in your field
Generating patent disclosures covering the algorithms created and the systems enabled We want you to join us because you have:
A getting-it-done attitude with a desire to both push the boundary of fundamental knowledge and turn it into great products.
Your experience and qualifications will also include:
A Bsc, Ph.D or M.Sc. in machine learning or a quantitative field and at least 1 year of industrial R&D experience.
Experience of scientific programming and libraries relevant to your field, for example: PyTorch, TensorFlow, Caffe, OpenCV, etc.
Experience with at least one of the following programming languages, for example, Python, C++, etc.
We are the changemakers. You will be joining a team of builders, doers and problem solvers. Our business was established 50 years ago, and we continue to innovate and disrupt the enterprise technology industry. We employ 7000 changemakers across 100 locations and our customers include some of the biggest businesses around the world who rely on our solutions to offer better patient care, improve customer experience and streamline their processes.
Benefits:
25 days paid holiday + public holidays
Continuous training & development including 24/7 access to an online training platform
Reward & recognition scheme
Pension scheme with up to 7% matched contribution
Private healthcare
Up to 4 days paid time off for charity/voluntary work
Computer Vision Researcher
Overview
The computer vision algorithm researcher II will work to advance exploratory research and development projects in computer vision and machine learning and related fields to create highly innovative customer solutions. The algorithm researcher will develop perception & learning systems using 2Dand 3D visioning systems that are Enterprise-grade and ready for real world deployment. Work is evaluated upon completion to ensure objectives have been met.
As a Computer Vision and Ai Researcher, you will have a unique opportunity to use your creativity both to understand the latest cutting-edge research and to tailor it for real-world applications. You will face a variety of challenges and will have access to the best hardware to do the job. We are focusing on using AI to help solve real-world problems on real-world data. This means embracing noise and complexity, both at the data level and at the methodological level. Your work will span from real-world data handling to the most advanced methods such as transfer learning, generative models, reinforcement learning, etc. with a focus on understanding quickly and experimenting even faster. You will collaborate closely both with software developers and research scientists, wielding the tools of both.
You will have experience in machine learning, either through your studies or industrial R&D projects and work to advance exploratory research and development projects in computer vision and machine learning and related fields to build highly innovative customer solutions. The algorithm researcher will develop perception & learning systems using 2D and 3D visioning systems that are Enterprise-grade and ready for real world deployment.
You want to join us because you are passionate about:
Research and applications of machine learning.
Collecting, quality checking, and analysing huge datasets.
Using and discovering the best tools to build practical AI models.
Staying up-to-date on the scientific literature in your field
Generating patent disclosures covering the algorithms created and the systems enabled We want you to join us because you have:
A getting-it-done attitude with a desire to both push the boundary of fundamental knowledge and turn it into great products.
Your experience and qualifications will also include:
A Bsc, Ph.D or M.Sc. in machine learning or a quantitative field and at least 1 year of industrial R&D experience.
Experience of scientific programming and libraries relevant to your field, for example: PyTorch, TensorFlow, Caffe, OpenCV, etc.
Experience with at least one of the following programming languages, for example, Python, C++, etc.
We are the changemakers. You will be joining a team of builders, doers and problem solvers. Our business was established 50 years ago, and we continue to innovate and disrupt the enterprise technology industry. We employ 7000 changemakers across 100 locations and our customers include some of the biggest businesses around the world who rely on our solutions to offer better patient care, improve customer experience and streamline their processes.
Benefits:
25 days paid holiday + public holidays
Continuous training & development including 24/7 access to an online training platform
Reward & recognition scheme
Pension scheme with up to 7% matched contribution
Private healthcare
Up to 4 days paid time off for charity/voluntary work
Computer Vision Researcher
Overview
The computer vision algorithm researcher II will work to advance exploratory research and development projects in computer vision and machine learning and related fields to create highly innovative customer solutions. The algorithm researcher will develop perception & learning systems using 2Dand 3D visioning systems that are Enterprise-grade and ready for real world deployment. Work is evaluated upon completion to ensure objectives have been met.
Responsibilities
Development of computer vision and machine learning algorithms and applications
Develop proof-of-concept implementations, prototypes and demos to fine-tune technologies to practical, state-of-the-art systems.
Participate and provide input in project code reviews
Perform experiments and evaluations to quantify and validate improvements made to existing systems.
Receive general instructions to routine work, new projects or assignments
Take responsibility for communicating with different engineers (embedded systems, backend server, computer vision) to continuously improve and optimize implemented algorithms
Create automated software tests and provide support to trouble shoot software issues when needed
Documentation of algorithm
Qualifications
Bachelor or Masters in computer vision or machine learning;
2-5 years work experience
Basic foundation in computer vision; key areas of interest include at least 2 of the following: object detection, tracking and recognition; multiple view geometry, 3D computer vision and SfM/SLAM; activity recognition
Scripting and prototyping languages: Python, Matlab, C/C++
Generic packages: OpenCV, Point Cloud Library
Experience with Linux, Windows and ROS platforms
Ability to grasp new concepts quickly
Ability to adapt and use these patterns in innovative ways to develop custom solutions
Ability to take initiative on issues and report results instead of waiting for task lists
Good presentation skills, both prepared and "on demand" talks
Machine Learning Researcher
Overview
The principal reinforcement learning researcher will lead advanced exploratory research and development projects in reinforcement learning and machine learning and related fields to create highly innovative customer solutions.
Responsibilities
Essential Duties and Responsibilities:
· Conduct theoretical/empirical research and development into sequential decision making and large-scale, open-ended learning.
· Prototype and productize microservices using Python or C++.
· Participate in daily standups in adherence to typical software development life-cycle.
· Contribute to the research community by publishing papers in machine learning (JMLR, ICLR, NeurIPS, ICML, ACL, AISTAS and AAMAS).
· Work with external collaborators (universities and startups) and foster relationships in reinforcement learning domains, multi-agents systems, control systems, game theory, and other topics of relevance to machine learning and theoretical statistics.
· Contribute research that can be applied to Zebra product development
· Superior verbal and written communication skills, including explaining the technology and its business/customer implications to executives and customers.
· Individual Contributor position that requires mentorship of PhD level scientists and engineers. Note: The statements herein are intended to describe the general nature and level of work being performed by employees and are not to be construed as an exhaustive list of responsibilities, duties, abilities, and skills required of personnel so classified.
Qualifications
Minimum Education:
o PhD degree in Computer Science, Engineering, Mathematics or Statistics.
o Postdoctoral training in machine learning.
o A proven record of publications (at least a few first-author paper in reputed conferences and journals – such as NeurIPS, COLT, UAI, ICML, AAMAS, AISTATS, JMLR, PAMI, and others).
Minimum Work Experience (years):
o 8-10
Key Skills and Competencies:
o Proficient in theoretical analysis of machine learning algorithms, esp. deep reinforcement learning algorithms.
o Experience in multi-agent reinforcement learning.
o Knowledge of Bayesian statistics.
o Hands-on experience with at least one of the following: Python, C/C++, Java, etc. (PyTorch and Tensorflow).
o Professional experience in microservices-based deployment of AI algorithms on cloud platforms (GCP, AWS or Azure).
o Experience in computer vision is desirable but not necessary.
Development of computer vision and machine learning algorithms and applications
Develop proof-of-concept implementations, prototypes and demos to fine-tune technologies to practical, state-of-the-art systems.
Participate and provide input in project code reviews
Perform experiments and evaluations to quantify and validate improvements made to existing systems.
Receive general instructions to routine work, new projects or assignments
Take responsibility for communicating with different engineers (embedded systems, backend server, computer vision) to continuously improve and optimize implemented algorithms
Create automated software tests and provide support to trouble shoot software issues when needed
Documentation of algorithm
Qualifications
Bachelor or Masters in computer vision or machine learning;
2-5 years work experience
Basic foundation in computer vision; key areas of interest include at least 2 of the following: object detection, tracking and recognition; multiple view geometry, 3D computer vision and SfM/SLAM; activity recognition
Scripting and prototyping languages: Python, Matlab, C/C++
Generic packages: OpenCV, Point Cloud Library
Experience with Linux, Windows and ROS platforms
Ability to grasp new concepts quickly
Ability to adapt and use these patterns in innovative ways to develop custom solutions
Ability to take initiative on issues and report results instead of waiting for task lists
Good presentation skills, both prepared and "on demand" talks
Machine Learning Researcher
Overview
The principal reinforcement learning researcher will lead advanced exploratory research and development projects in reinforcement learning and machine learning and related fields to create highly innovative customer solutions.
Responsibilities
Essential Duties and Responsibilities:
· Conduct theoretical/empirical research and development into sequential decision making and large-scale, open-ended learning.
· Prototype and productize microservices using Python or C++.
· Participate in daily standups in adherence to typical software development life-cycle.
· Contribute to the research community by publishing papers in machine learning (JMLR, ICLR, NeurIPS, ICML, ACL, AISTAS and AAMAS).
· Work with external collaborators (universities and startups) and foster relationships in reinforcement learning domains, multi-agents systems, control systems, game theory, and other topics of relevance to machine learning and theoretical statistics.
· Contribute research that can be applied to Zebra product development
· Superior verbal and written communication skills, including explaining the technology and its business/customer implications to executives and customers.
· Individual Contributor position that requires mentorship of PhD level scientists and engineers. Note: The statements herein are intended to describe the general nature and level of work being performed by employees and are not to be construed as an exhaustive list of responsibilities, duties, abilities, and skills required of personnel so classified.
Qualifications
Minimum Education:
o PhD degree in Computer Science, Engineering, Mathematics or Statistics.
o Postdoctoral training in machine learning.
o A proven record of publications (at least a few first-author paper in reputed conferences and journals – such as NeurIPS, COLT, UAI, ICML, AAMAS, AISTATS, JMLR, PAMI, and others).
Minimum Work Experience (years):
o 8-10
Key Skills and Competencies:
o Proficient in theoretical analysis of machine learning algorithms, esp. deep reinforcement learning algorithms.
o Experience in multi-agent reinforcement learning.
o Knowledge of Bayesian statistics.
o Hands-on experience with at least one of the following: Python, C/C++, Java, etc. (PyTorch and Tensorflow).
o Professional experience in microservices-based deployment of AI algorithms on cloud platforms (GCP, AWS or Azure).
o Experience in computer vision is desirable but not necessary.
--
Prof Sergio A Velastin PhD MSc FIET CEng SMIEEE
Professor of Applied Computer Vision
https://scholar.google.es/citations?user=FsE86kwAAAAJ&hl=en
sergio.velastin@ieee.org
ORCID: 0000-0001-6775-1737
Profesor Honorifico, Universidad Carlos III de Madrid, Spain
Visiting Professor, School of Electronic Engineering and Computer Science,
Queen Mary University of London
✔️ @ApplyTime
Prof Sergio A Velastin PhD MSc FIET CEng SMIEEE
Professor of Applied Computer Vision
https://scholar.google.es/citations?user=FsE86kwAAAAJ&hl=en
sergio.velastin@ieee.org
ORCID: 0000-0001-6775-1737
Profesor Honorifico, Universidad Carlos III de Madrid, Spain
Visiting Professor, School of Electronic Engineering and Computer Science,
Queen Mary University of London
✔️ @ApplyTime
scholar.google.es
Prof. Sergio A Velastin FIET SMIEE
Visiting Professor, Queen Mary University of London - Cited by 9,477 - Human Action Recognition - Visual Surveillance
PhD Opening in Human-guided Robotic Behavior Learning
The Unmanned Systems Lab at University of Texas at San Antonio has 1-2 fully funded PhD openings in the area of human-guided robotic behavior learning. The main research goal is to develop new human-guided reinforcement learning algorithms to enable fast/efficient robotic behavior learning. These challenges include, but not are limited to, human behavior learning/modeling, reinforcement learning under limited human guidance, multi-agent reinforcement learning, human-robot interactive learning.
Position denoscription:
Required
- A Bachelor’s degree in electrical and computer engineering, computer science, mathematics, or a related field;
- Strong background in mathematics, statistics, and machine learning;
- Excellent writing and communication skills;
- Proficiency in C++ or Python.
Preferred
- Master’s degree
- Experience on Robot Operating System (ROS), reinforcement learning, and computer vision
- Experience on Tensorflow, Keras, or PyTorch.
- Demonstrated research experience (i.e., projects or publications)
How to apply:
Send the following documents in a single PDF file
- One-page cover letter describing your interest, goal, and how your background fits well;
- CV or resume
- Trannoscripts
to yongcan.cao@utsa.edu
✔️ @ApplyTime
The Unmanned Systems Lab at University of Texas at San Antonio has 1-2 fully funded PhD openings in the area of human-guided robotic behavior learning. The main research goal is to develop new human-guided reinforcement learning algorithms to enable fast/efficient robotic behavior learning. These challenges include, but not are limited to, human behavior learning/modeling, reinforcement learning under limited human guidance, multi-agent reinforcement learning, human-robot interactive learning.
Position denoscription:
Required
- A Bachelor’s degree in electrical and computer engineering, computer science, mathematics, or a related field;
- Strong background in mathematics, statistics, and machine learning;
- Excellent writing and communication skills;
- Proficiency in C++ or Python.
Preferred
- Master’s degree
- Experience on Robot Operating System (ROS), reinforcement learning, and computer vision
- Experience on Tensorflow, Keras, or PyTorch.
- Demonstrated research experience (i.e., projects or publications)
How to apply:
Send the following documents in a single PDF file
- One-page cover letter describing your interest, goal, and how your background fits well;
- CV or resume
- Trannoscripts
to yongcan.cao@utsa.edu
✔️ @ApplyTime
Researcher in Federated Learning (IHU Strasbourg)
About the group
The research group CAMMA at IHU Strasbourg is looking for a researcher in machine learning. The successful candidate will perform research in federated learning for medical data to develop generalizable and efficient ML (e.g., self-supervision, domain shift, improved data utility detection). The candidate must have a PhD in computer science with background in machine learning and have demonstrated his research abilities with peer-reviewed publications in top conferences and journals (e.g. CVPR, ICLR, MICCAI, etc.). The ideal candidate has proven experience in Federated Learning through publication(s) and/or code (e.g. Github). Background in medical data (e.g., imaging, health records, genomics, etc.) is a plus but not a requirement. The candidate will have the opportunity to work with a multidisciplinary team of researchers and clinicians in France and Europe and shape research. In parallel, they will also have the chance to work together with top industry partners through MLCommons to develop better tools for medical AI and thus gain major visibility. Salary is competitive, and appointment duration is 2 years with the possibility of extension.
About the Project
CLINNOVA is a European initiative of the Greater Region (http://www.granderegion.net/) which groups together French region Grand Est, Belgian Federation Wallonia-Brussels and Ostbelgien, German Saarland and Rhineland-Palatinate as well as the Grand Duchy of Luxembourg. CLINNOVA project aims at unlocking the potential of Artificial Intelligence (AI) and data science in healthcare, with the ambition to establish a European standard model, which is sovereign, open, and interoperable. The overall goal of CLINNOVA is to enable data-driven health environment for AI solutions, which is based both on infrastructure investment and coordination between clinical players. The initiative aims to create a federated infrastructure of large prospective normalized multimodal medical data (e.g., biobanking, imaging) between participating institutes with a focus on autoimmune, inflammatory and cancer diseases. Research and development of AI algorithms on this massive amount of federated data is a unique exciting opportunity from computer science and clinical perspectives.
Skills/Qualifications
Perform research in Federated Learning algorithms
Publish peer-reviewed papers
Participate in research meetings with top industry partners and contribute code to open research community (e.g. MLCommons)
Mentor interns
Machine Learning Background
Python or other language
PyTorch, Keras, or Tensorflow
Please submit your cover letter and CV at alexandros.karargyris [at] ihu-strasbourg.eu
✔️ @ApplyTime
About the group
The research group CAMMA at IHU Strasbourg is looking for a researcher in machine learning. The successful candidate will perform research in federated learning for medical data to develop generalizable and efficient ML (e.g., self-supervision, domain shift, improved data utility detection). The candidate must have a PhD in computer science with background in machine learning and have demonstrated his research abilities with peer-reviewed publications in top conferences and journals (e.g. CVPR, ICLR, MICCAI, etc.). The ideal candidate has proven experience in Federated Learning through publication(s) and/or code (e.g. Github). Background in medical data (e.g., imaging, health records, genomics, etc.) is a plus but not a requirement. The candidate will have the opportunity to work with a multidisciplinary team of researchers and clinicians in France and Europe and shape research. In parallel, they will also have the chance to work together with top industry partners through MLCommons to develop better tools for medical AI and thus gain major visibility. Salary is competitive, and appointment duration is 2 years with the possibility of extension.
About the Project
CLINNOVA is a European initiative of the Greater Region (http://www.granderegion.net/) which groups together French region Grand Est, Belgian Federation Wallonia-Brussels and Ostbelgien, German Saarland and Rhineland-Palatinate as well as the Grand Duchy of Luxembourg. CLINNOVA project aims at unlocking the potential of Artificial Intelligence (AI) and data science in healthcare, with the ambition to establish a European standard model, which is sovereign, open, and interoperable. The overall goal of CLINNOVA is to enable data-driven health environment for AI solutions, which is based both on infrastructure investment and coordination between clinical players. The initiative aims to create a federated infrastructure of large prospective normalized multimodal medical data (e.g., biobanking, imaging) between participating institutes with a focus on autoimmune, inflammatory and cancer diseases. Research and development of AI algorithms on this massive amount of federated data is a unique exciting opportunity from computer science and clinical perspectives.
Skills/Qualifications
Perform research in Federated Learning algorithms
Publish peer-reviewed papers
Participate in research meetings with top industry partners and contribute code to open research community (e.g. MLCommons)
Mentor interns
Machine Learning Background
Python or other language
PyTorch, Keras, or Tensorflow
Please submit your cover letter and CV at alexandros.karargyris [at] ihu-strasbourg.eu
✔️ @ApplyTime
PhD Positions: A Practical Theory of Computation for Modern Neural Network Architectures
The Natural Language Processing Group at Linköping University and the Foundations of Language Processing Group at Umeå University are announcing two (2) fully-funded PhD Positions in Computer Science.
The positions are announced in the project “A Practical Theory of Computation for Modern Neural Network Architectures”, headed by Marco Kuhlmann (Linköping) and Frank Drewes (Umeå). The aim of this project is to develop a practical theory of computation for modern neural network architectures by combining methods from theoretical computer science with empirical validation in natural language processing (NLP).
For more information about the project and the positions, please see the following web page:
https://liu-nlp.github.io/wasp-2021/
The application deadline is 2021-09-30.
Best regards
Marco Kuhlmann
Professor
Department of Computer and Information Science
Linköping University
Sweden
✔️ @ApplyTime
The Natural Language Processing Group at Linköping University and the Foundations of Language Processing Group at Umeå University are announcing two (2) fully-funded PhD Positions in Computer Science.
The positions are announced in the project “A Practical Theory of Computation for Modern Neural Network Architectures”, headed by Marco Kuhlmann (Linköping) and Frank Drewes (Umeå). The aim of this project is to develop a practical theory of computation for modern neural network architectures by combining methods from theoretical computer science with empirical validation in natural language processing (NLP).
For more information about the project and the positions, please see the following web page:
https://liu-nlp.github.io/wasp-2021/
The application deadline is 2021-09-30.
Best regards
Marco Kuhlmann
Professor
Department of Computer and Information Science
Linköping University
Sweden
✔️ @ApplyTime
Ph.D. student / Postdoc position in machine learning with spiking neurons at University of Bremen, Germany
You looked at our paper Back-Propagation Learning in Deep Spike-By-Spike Networks ( https://www.frontiersin.org/articles/10.3389/fncom.2019.00055/full ) and thought "Interesting idea but I can improve that!" then you may want to tell us your idea... You could end up working on it as a Ph.D. student / Postdoc for the next three years in Bremen, Germany.
-------------
The Computational Neuroscience group of Klaus Pawelzik invites applications for an open Ph.D. student / Postdoc position (E13 TV-L 100% for 3 years; all genders welcome) in the project "Efficient Implementation of Spike-by-Spike Neural Networks using Stochastic and Approximative Techniques". We are looking for a person with a strong background in mathematics, programming, and machine learning as well as an intense interest in neuroscience. Someone who is not afraid of cooperating with engineers, since this is a joint project with a focus on hardware development.
The overarching goal of our project is to improve the efficiency of spiking artificial neural networks using hardware and algorithmic approximation techniques. Specifically, the project focuses on Spike-by-Spike networks since they offer a balance between computational requirements and biological-realism which keeps the advantages of the biological networks while enabling a compact technical realization. To fully take advantage of the unique features of SbS in terms of robustness and sparseness, dedicated hardware architectures are required.
You would join in with numerical simulations, theoretical analyses, as well as through the development of new ideas and approaches for boosting the performance and capabilities of the Spike-By-Spike model. Furthermore, you would also work on combining Spike-By-Spike networks with non spiking deep neuronal networks into hybrid models.
The details can be found at http://www.neuro.uni-bremen.de/content/open-position-sbs
✔️ @ApplyTime
You looked at our paper Back-Propagation Learning in Deep Spike-By-Spike Networks ( https://www.frontiersin.org/articles/10.3389/fncom.2019.00055/full ) and thought "Interesting idea but I can improve that!" then you may want to tell us your idea... You could end up working on it as a Ph.D. student / Postdoc for the next three years in Bremen, Germany.
-------------
The Computational Neuroscience group of Klaus Pawelzik invites applications for an open Ph.D. student / Postdoc position (E13 TV-L 100% for 3 years; all genders welcome) in the project "Efficient Implementation of Spike-by-Spike Neural Networks using Stochastic and Approximative Techniques". We are looking for a person with a strong background in mathematics, programming, and machine learning as well as an intense interest in neuroscience. Someone who is not afraid of cooperating with engineers, since this is a joint project with a focus on hardware development.
The overarching goal of our project is to improve the efficiency of spiking artificial neural networks using hardware and algorithmic approximation techniques. Specifically, the project focuses on Spike-by-Spike networks since they offer a balance between computational requirements and biological-realism which keeps the advantages of the biological networks while enabling a compact technical realization. To fully take advantage of the unique features of SbS in terms of robustness and sparseness, dedicated hardware architectures are required.
You would join in with numerical simulations, theoretical analyses, as well as through the development of new ideas and approaches for boosting the performance and capabilities of the Spike-By-Spike model. Furthermore, you would also work on combining Spike-By-Spike networks with non spiking deep neuronal networks into hybrid models.
The details can be found at http://www.neuro.uni-bremen.de/content/open-position-sbs
✔️ @ApplyTime
Frontiers
Back-Propagation Learning in Deep Spike-By-Spike Networks
Artificial neural networks (ANNs) are important building blocks in technical applications. They rely on noiseless continuous signals in stark contrast to the discrete action potentials stochastically exchanged among the neurons in real brains. We propose…
PhD scholarship in NLP
PhD scholarships are available in the area of machine learning with application to sentiment analysis, social networks and facial expressions.
Preference will be given to candidates with a masters, at least one research publication and 6 months full time work experience.
Application deadline is end of 30th Sep 2021.
https://www.jcu.edu.au/graduate-research-school/how-to-apply
For further discussion kindly email your resume to i.chaturvedi@uq.edu.au
✔️ @ApplyTime
PhD scholarships are available in the area of machine learning with application to sentiment analysis, social networks and facial expressions.
Preference will be given to candidates with a masters, at least one research publication and 6 months full time work experience.
Application deadline is end of 30th Sep 2021.
https://www.jcu.edu.au/graduate-research-school/how-to-apply
For further discussion kindly email your resume to i.chaturvedi@uq.edu.au
✔️ @ApplyTime
Dear All,
We have a postdoc position at City University of Hong Kong. The research will be on reinforcement learning in medical/robotics applications. To apply, please send your CV to enek...@cityu.edu.hk
Contact information:
Ehsan Nekouei
Email: enek...@cityu.edu.hk
Web: http://www.ee.cityu.edu.hk/~enekouei/index.html
Best,
Ehsan
✔️ @ApplyTime
We have a postdoc position at City University of Hong Kong. The research will be on reinforcement learning in medical/robotics applications. To apply, please send your CV to enek...@cityu.edu.hk
Contact information:
Ehsan Nekouei
Email: enek...@cityu.edu.hk
Web: http://www.ee.cityu.edu.hk/~enekouei/index.html
Best,
Ehsan
✔️ @ApplyTime