Postdoctoral Researcher in Reinforcement Learning: The ODI group at the Institute of Machine Learning is seeking highly motivated postdoctoral researchers with expertise in reinforcement learning. This is a fixed-term position based in Zurich, offering an opportunity to collaborate in an inspiring research environment.
Designation
Postdoctoral Researcher in Reinforcement Learning
Research Area
- Theory of Online Learning and Reinforcement Learning
- Multi-Agent Reinforcement Learning
- RLHF and Alignment in Large Language Models
- Representation Learning in Reinforcement Learning
- Transfer Learning in Multi-Task Bandits and RL
- Statistical or Generative Decision Making
Location
Zurich, ETH Zurich
Eligibility/Qualification
- A PhD degree in Computer Science or a related field.
- A solid foundation in reinforcement learning.
- Proven research experience with first-authored publications in peer-reviewed conferences (e.g., ICML, ICLR, NeurIPS, CoRL, ICRA, AAAI, AAMAS).
- A collaborative mindset and ability to work within a multidisciplinary team.
- Optional: Experience with large-scale machine learning codebases.
Job Description
The selected candidate will work on various aspects of reinforcement learning, tailoring project scope according to their strengths and expertise. Responsibilities include close collaboration with the research supervisor and ODI team members and mentoring Master’s or Bachelor’s thesis projects.
How to Apply
Interested candidates should submit the following documents through our online application portal:
- Curriculum Vitae
- Complete publication list
- Brief research statement (2 pages) outlining your expertise and interests
- Contact information for 2-3 references
- Copies of academic certificates
Last Date to Apply
Applications are accepted on a rolling basis until the position is filled. Applicants are encouraged to apply as soon as possible.
For any questions regarding the position, please contact Dr. Zebang Shen at zebang.shen@inf.ethz.ch.
Join us at ETH Zurich, one of the world’s leading universities in science and technology, and be a part of groundbreaking research in reinforcement learning!
