PhD Position in Reinforcement Learning Theory: Delft University of Technology (TU Delft) is inviting applications for a PhD position focused on the theoretical aspects of reinforcement learning. This scholarship will provide talented candidates with the opportunity to engage in groundbreaking research aimed at developing new algorithms and improving our understanding of reinforcement learning methods, tackling challenging problems in sequential decision-making.
Designation
PhD Candidate in Reinforcement Learning Theory
| Category | Details |
|---|---|
| Research Area | Theoretical Machine Learning, Sequential Decision-Making, Bandit Problems, Reinforcement Learning |
| Location | TU Delft, Delft, Netherlands |
| Eligibility/Qualification | Master’s degree in Mathematics, Computer Science, Physics, or a related field; strong mathematical background; knowledge of statistics and optimization; background in machine learning is a plus. |
| Salary Range | €2872 – €3670 per month |
| Hours per week | 36-40 |
| FTE | 1 |
| Contract Duration | Initial 1.5 years with assessment, followed by 2.5 years contract based on performance |
Job Description
As a PhD candidate, you will:
- Analyze and design learning algorithms with formal performance guarantees.
- Work on the development of advanced function approximation methods and robust reinforcement learning techniques.
- Collaborate within the Sequential Decision-Making group and engage deeply in the theoretical aspects of reinforcement learning.
- Utilize your skills in advanced mathematical and statistical techniques.
How to Apply
Interested candidates should apply online by uploading the following documents:
- CV
- Motivational letter addressed to Ms. I.M. Olkhovskaia
Note: Applications sent by email or post will not be processed.
Last Date to Apply
December 8, 2024
For more information about the vacancy or the application process, you can reach out to Julia Olkhovskaya at I.M.Olkhovskaia@tudelft.nl.
Join us at TU Delft to contribute to pioneering research in reinforcement learning and make a significant impact in the field of machine learning!
