Open positions
Postdoctoral positions
Applications openI am seeking postdoctoral researchers to work on reinforcement learning and/or online learning, with a flexible starting date. These are fully funded, full-time research positions, with no mandatory teaching duties (although teaching is possible), and an expected duration of two years.
Successful candidates will work under my direct supervision at Politecnico di Milano, with opportunities to collaborate with the rest of the research group and other researchers. Positions are funded by the FIS2 Starting Grant; precise research topics are flexible as long as they align with reinforcement learning or online learning.
Ideal profile
Ideal candidates have a strong publication record in machine learning, consistent with venues such as ICML, NeurIPS, ICLR, AISTATS, COLT, and JMLR. I welcome both theoretical and experimental research profiles.
Theory-oriented candidates
Experience in reinforcement learning theory, bandit problems, and online learning theory.
Experimental candidates
Experience in experimental reinforcement learning, strong programming skills, and frameworks such as PyTorch.
Strong written and spoken English communication skills are also important.
How to apply
Send the following documents by email with the subject Postdoc Application – YOUR LAST NAME:
- An up-to-date CV including your publications.
- Contact details for at least two references.
- A motivation letter describing your research interests and fit with the group. (Hint: It is a good idea to describe how your interests align with my research in general.)
- Links to one or two papers from your previous work you are particularly proud of.
- Any other relevant documents.
Applications are reviewed as they arrive. If no response is received within two months, the application has not been shortlisted.
Send your application torl3polimi.applications@gmail.comResearch environment
The team is part of the RL3 research group at DEIB, and participates in the ELLIS Unit Milan. The group is active in research on reinforcement learning, regularly publishing at leading international venues.
