Fully funded robotics Ph.D. positions in Fall 2022 at George Mason University
The RobotiXX lab at the Department of Computer Science, George Mason University in the Washington, D.C. area (https://www2.gmu.edu/), is looking for self-motivated Ph.D. students in the area of robotics starting Fall 2022. The candidate will be supported with full tuition and stipends.



At RobotiXX lab, we perform robotics research at the intersection of motion planning and machine learning, with a specific focus on deployable field robotics. Any candidate with relevant experience in robotics, motion planning, and machine learning is encouraged to apply. Hands-on knowledge in robotics hardware, field experience, and a good publication record is strongly preferred.



The selected candidates will conduct independent research to develop highly capable and intelligent mobile robots that are robustly deployable in the real world with minimal human supervision, and publish papers in top-tier robotics conferences and journals. To apply, please visit George Mason University's admission website (https://www2.gmu.edu/admissions-aid) and send a single PDF of the following documents to Dr. Xuesu Xiao, incoming Assistant Professor in Fall 2022 (xiao@cs.utexas.edu): 1. cover letter, 2. curriculum vitae, 3. trannoscript, 4. three references, 5. relevant publications, 6. other relevant materials, with the subject line “[jobs] YOUR AFFILIATION - YOUR NAME”.



For more information, please see the lab’s research statement (https://www.cs.utexas.edu/~xiao/xuesu_website_files/Research_Statement.pdf), YouTube Channel (https://www.youtube.com/channel/UCCePdJzWg35eml4WRGkX3GQ/videos), and website (https://www.cs.utexas.edu/~xiao/).



Thanks

Xuesu Xiao



-----------------------

Xuesu Xiao, Ph.D.

--

Incoming Assistant Professor (Fall 2022)

Department of Computer Science
George Mason University
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Research scientist positions in ML at NEC Labs in Princeton
The NEC Labs Machine Learning Department in Princeton, NJ, has openings for
researchers with a passion for developing the next generation of
machine intelligence. Expertise in machine learning with a proven
track record of original research as well as a keen sense for
developing practical applications are prerequisites for this position.

The Machine Learning Department has been at the forefront of research
in such areas as deep learning, support vector machines, and semantic
analysis for almost two decades. Many technologies developed in our
group have been released as innovative products and services of NEC,
such as systems for recruiting, surveillance, inspection of
manufactured goods, and digital pathology. In addition to contributing
to NEC’s business, our research is published in premier venues. Among
the challenges we are tackling now are, how to move machine learning
to more abstract reasoning, and how this can enable new applications
in smart manufacturing, safe cities, and personalized healthcare.
http://www.nec-labs.com/research-departments/machine-learning/machine-learning-home


POSITION REQUIREMENTS

• PhD in computer science, statistics, or equivalent
• Research experience in machine learning with strong publication record
• Strong algorithm and numeric computation background
• Programming experience in Python, Lua, C++, or other languages
• Experience with deep learning libraries and platforms a plus, e.g.
PyTorch, TensorFlow, or Caffe


For more information about NEC Labs, please access:
www.nec-labs.com/research-departments/machine-learning/machine-learning-home
and submit your CV and research statement through our career center at
https://www.appone.com/MainInfoReq.asp?R_ID=4141735
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Research Assistant Position at Columbia (NYC)

Research Assistant Position

The Laboratory for Computational Psychiatry and Translational Neuroscience (PI: Kiyohito Iigaya) at Columbia University Irving Medical Center is seeking a full-time Research Assistant position. We are a new laboratory interested in advancing our understanding of fundamental neuroscience and translating the findings to clinical applications using computational methods.

The ideal candidate will develop and perform online (e.g., Amazon M-Turk) and in-person (e.g., fMRI) studies with human volunteers. We are particularly interested in a well-organized individual who has excellent programming skills.

Our Lab is located at the New York State Psychiatric Institute and Department of Psychiatry at Columbia University Irving Medical Center in NYC.

Click here to apply.



https://opportunities.columbia.edu/en-us/job/520298/research-assistant



Best wishes,

Kyo Iigaya



---------------------------------

Kiyohito Iigaya, Ph.D.
Assistant Professor of Neurobiology (in Psychiatry)
Columbia University Irving Medical Center
ki2151@columbia.edu
✔️ @ApplyTime

Post-doc in Conversational Recommendation at U. of Toronto
I have a new opening for a post-doc in Conversational Recommendation at U. of Toronto.

The research will focus on machine learning methodologies leveraging recent advances in recommender systems and applied NLP.

Post-docs are expected to publish in top research venues, to produce high-quality deliverable code for use by research funding partners, and to help provide supervision for a team of graduate students working on the same project.

Deadline: the candidate should be able to start on or before Feb 1, 2021.

If you are interested, please email ssanner@cs.toronto.edu with the following:
(a) your CV (clearly listing all publications and date PhD granted / expected),
(b) your github public account link with projects that I can browse (if you don't have this, please do not apply),
(c) 1-2 sentences in your email stating your interests in conversational recommendation and why you think you would be appropriate for the position.

Dr. Scott P. Sanner

Associate Professor, Industrial Engineering
Cross-appointed, Computer Science
Faculty Affiliate, Vector Institute
University of Toronto, Toronto, ON, Canada
Email: ssanner@cs.toronto.edu
Website: http://d3m.mie.utoronto.ca/
✔️ @ApplyTime

Dear all,

Several faculty members of the Vector Institute in Canada work on reinforcement learning, sequential decision making, and closely related research areas. Each of us is affiliated with one of the Canadian universities, but we are all affiliated with the Vector Institute and often collaborate with each other. Most of our students are located at the Vector Institute, which is a thriving environment for machine learning research.

We plan to recruit several graduate students this year. If you are interested in reinforcement learning research, please apply through our respective departments. We welcome both domestic and international students.

The name of faculty members who work on RL-related topics are as follows. Please check their webpages to find the best match based on your interests, and particular instructions that they may have for prospective students.

Amir-massoud Farahmand (Department of Computer Science, University of Toronto). Interests: Theoretical RL, Model-based RL, Risk and Robustness (Prospective Students)

Angela Schoellig (Institute for Aerospace Studies, University of Toronto). Interests: Robot Control and Learning; Reinforcement Learning for Robotics; Mobile Manipulation; Self Driving and Flying

Animesh Garg (Department of Computer Science, University of Toronto). Interests: Generalizable Autonomy for Robotics, Reinforcement Learning, Optimal Control, Causal Decision Making, Neural Architectures for Decision Making (Prospective Students)

Florian Shkurti (Department of Computer Science, University of Toronto). Interests: Machine learning for planning and control, Robotics, Inverse RL, Imitation Learning

Jeff Clune (Computer Science, University of British Columbia). Interests: Deep Reinforcement Learning, AI-Generating Algorithms

Joseph J. Williams (Departments of Computer Science, Statistical Sciences, and Psychology, University of Toronto). Interests: Multi-armed bandits for healthcare and education (Prospective Students)

Pascal Poupart (School of Computer Science, University of Waterloo). Interests: Partially Observable Reinforcement Learning, Bayesian Reinforcement Learning, Causal Reinforcement Learning, Federated Reinforcement Learning, Object-Oriented Reinforcement Learning, Reinforcement Learning in Natural Language Processing

Daniel M. Roy (Department of Statistical Sciences and Computer Science, University of Toronto). Interests: Theory for worst case and adaptive online learning, bandits, and, in the future, RL. Prospective students should apply to both CS and Statistics, if appropriate.

Scott Sanner (Department of Mechanical and Industrial Engineering, Cross-appointed in Department of Computer Science, University of Toronto). Interests: Data-driven Decision Making, Sequential Decision Optimization

Sheila McIlraith (Department of Computer Science, University of Toronto). Interests: Sequential decision making and reinforcement learning, Program synthesis, Human-compatible AI


Please note that, because of the high volume of inquiries, some of the listed faculty may not be able to respond to individual emails from prospective students. This should not be interpreted as a lack of interest. It is sufficient to mention their names in your application, and they will closely look at your application.


Each department has its own webpage, admission deadline and requirements. Please check them for the updated information.

University of Toronto

Computer Science (Admission – Deadline: December 1)

Mechanical and Industrial Engineering (Admission – Deadline: Jan 1, 2022)

University of Toronto Institute for Aerospace Studies (Admission – Deadline: December 15, 2021 (fee); January 15, 2022 (application material))

University of Waterloo (Computer Science) (Admission – Deadline: December 15)

University of British Columbia (Computer Science) (Admission – Deadline: December 15)

In addition to these faculty members whose work has a significant focus on RL, there are many other researchers at the Vector Institute who work on other aspects of machine learning and deep learning, including fundamental algorithm design, representation learning, generative models, optimization, theory of ML and DL, computer vision, privacy, fairness, intersection of quantum computing and ML, and applications in healthcare, material design, music, etc. You can find their names here.


About Vector Institute

The Vector Institute is an independent non-profit corporation, with Faculty Members and Affiliates from the University of Toronto, University of Waterloo, University of Guelph, Dalhousie University, and other Canadian universities. It is supported with generous funding from the provincial and federal governments, as well as Canadian industry sponsors. The Vector Institute is located in the MaRS Discovery District building, spanning nearly the entire 7th floor and overlooking downtown Toronto and the beautiful Queen’s Park. On any given day, the Vector Institute houses over a hundred students, dozens of Faculty Members, supported with state-of-the-art compute power, and dedicated professional staff. The daily life and concentration of expertise in the Institute fosters collaboration and the exchange of ideas among its members through talks, seminar series, visitors, and tutorials. The Vector Institute’s vision is to drive excellence in the creation of artificial intelligence, to use it to foster economic growth, and to improve the lives of Canadians. To that end, the Vector Institute has close ties to both academia and industry.


Best Regards,
Amir-massoud Farahmand on behalf of my colleagues

PhD in Reinforcement Learning, Differential Privacy or Fairness

We are looking for a PhD student to join our group on reinforcement
learning and decision making under uncertainty more generally, at the
University of Neuchatel, Switzerland ( https://www.unine.ch/ ). We
are particularly interested in candidates with a strong mathematical
background. Prior research experience as documented by your Masters
thesis is required. Within the area, we are looking for candidates
with a strong research interest in the following fields

- Reinforcement learning and decision making under uncertainty:
1. Exploration in reinforcement learning.
2. Decision making nuder partial information.
3. Representations of uncertainty in decision making.
4. Theory of reinforcement learning (e.g. PAC/regret bounds)
5. Bayesian inference and approximate Bayesian methods.

- Social aspect of machine learning
1. Theory of differntial privacy.
2. Algorithms for differentially private machine learning.
3. Algorithms for fairness in machine learning.
4. Interactions between machine learning and game theory.
5. Inference of human models of fairness or privacy.

The main supervisor will be Christos Dimitrakakis <
https://sites.google.com/site/christosdimitrakakis >

Examples of our group's past and current research can be found on arxiv:
https://arxiv.org/search/?searchtype=author&query=Dimitrakakis%2C+C.
The student will have the opportunity to visit and work with other group
members at the University of Oslo, Norway (
https://www.mn.uio.no/ifi/english/people/aca/chridim/index.html ) and
Chalmers University of Technology, Sweden (
http://www.cse.chalmers.se/~chrdimi/ ). While the group is currently
geographically distributed, there will be plenty of opportunities for
exchanges.


The PhD candidate must have a strong technical background, including:

1. Thorough knowledge of calculus and linear algebra.
2. A good theoretical background in probability and statistics/machine
learning.
3. Practical experience with at least one programming language.

The candidate's background will be mainly assessed through their MSc
thesis and trannoscripts, and secondarily through an interview.


>>>> Application Information <<<<<

*Starting date* 1 Februrary 2022 or soon afterwards.
*Application deadline* 30 November 2021.

To apply sen an email to christos.dimitrakakis@gmail.com with the
subject 'PhD Neuchatel'.
An application must include:

1. A statement of research interests and motivation relevant to the
position.
2. A CV with a list of references.
3. Your MSc thesis or another research work demonstrating your academic
writing.
4. A degree trannoscript.

Feel free to include any other additional information.

Doctoral studies in Computational/Theoretical Neuroscience at NYU

New York University is home to a thriving interdisciplinary community of researchers using computational and theoretical approaches in neuroscience. We are interested in exceptional PhD candidates with strong quantitative training (e.g., physics, mathematics, engineering) coupled with a clear interest in scientific study of the brain.

A listing of faculty, sorted by their primary departmental affiliation, is given below. Doctoral programs are flexible, allowing students to pursue research across departmental boundaries. Nevertheless, admissions are handled separately by each department, and students interested in pursuing graduate studies should submit an application to the program that best fits their goals and interests.

Center for Neural Science (CNS), Graduate School of Arts & Sciences (deadline: 1 December)
[https://neuroscience.nyu.edu/program.html, and https://as.nyu.edu/cns/DoctoralProgram.html]
* SueYeon Chung (starting Sep 2022) - NeuroAI and geometry.
* Andre A. Fenton - Molecular, neural, behavioral, and computational aspects of memory.
* Paul W. Glimcher - Decision-making in humans and animals. Neuroeconomics.
* David Heeger (also in Psychology) - Computational neuroscience, vision, attention.
* Roozbeh Kiani - Vision and decision-making.
* Wei Ji Ma (also in Psychology) - Perception, working memory, and decision-making.
* Tony Movshon - Vision and visual development.
* Bijan Pesaran - Neuronal dynamics and decision-making.
* Alex Reyes - Functional interactions of neurons in a network.
* John Rinzel (also in Mathematics) - Biophysical mechanisms and theory of neural computation.
* Cristina Savin (also in the Center for Data Science) - Computational models of learning and memory, machine learning.
* Robert Shapley - Visual physiology and perception.
* Eero Simoncelli - Computational vision and audition.
* Xiao-Jing Wang - Computational neuroscience, decision-making and working memory, neural circuits.
* Alex Williams - Statistical analysis of neural data.

Neuroscience Institute, School of Medicine (deadline: 1 December)
[https://neuroscience.nyu.edu/program.html, and https://med.nyu.edu/departments-institutes/neuroscience/]
* Gyorgy Buzsaki - Rhythms in neural networks.
* Dmitri Chklovskii (also in the Simons Foundation) - Neural computation and connectomics.
* Biyu He - Large-scale brain dynamics underlying human cognition.
* Dmitry Rinberg - Sensory information processing in the behaving animal.
* Shy Shoham - Methods for controlling, imaging, and analyzing neural systems.
* Mario Svirsky - Auditory neural prostheses; experimental/computational studies of speech production/perception.

Psychology, Cognition & Perception program (deadline: 1 December)
[http://as.nyu.edu/psychology/graduate/phd-cognition-perception.html]
* Todd Gureckis - Memory, learning, and decision processes.
* Brendan Lake (also in the Center for Data Science) - Computational modeling of cognition, deep learning.
* Michael Landy - Computational approaches to vision.
* Laurence Maloney - Mathematical approaches to psychology and neuroscience.
* Denis Pelli - Visual object recognition.
* Jonathan Winawer - Visual perception and memory.

Mathematics (deadline: 4 January)
[http://math.nyu.edu/degree/phd/]
* David McLaughlin - Nonlinear wave equations, computational visual neuroscience.
* Aaditya Rangan - Computational neurobiology, numerical analysis.
* Charles Peskin - Mathematical biology.
* Daniel Tranchina - Information processing in the retina.
* Lai-Sang Young - Dynamical systems, statistical physics, computational modeling and theoretical neuroscience.

Data Science (deadline: 12 December)
[https://cds.nyu.edu/phd-program/]
* Joan Bruna (also in Computer Science) - Machine learning, signal/image processing.
* Kyungyun Cho (also in Computer Science) - Machine learning, natural language processing.
* Carlos Fernandez-Granda (also in Mathematics) - Optimization methods for medical imaging, neuroscience, computer vision.

Physics (deadline: 18 December)
[https://as.nyu.edu/physics/programs/graduate.html]
* Marc Gershow - Perception, decision-making, and learning in neural circuits.

Computer Science (deadline: 12 December)
[http://www.cs.nyu.edu/home/phd/]
* Davi Geiger - Computational vision and learning.
* Yann LeCun - Machine learning, computer vision, robotics, computational neuroscience.

Economics (deadline: 18 December)
[https://as.nyu.edu/econ/graduate/phd.html]
* Andrew Caplin - Economic theory, neurobiology of decision.
* Andrew Schotter - Experimental economics, game theory, neurobiology of decision.
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