Italy
- Commitment & contract: at least 2 Years_
- Location: IIT Erzelli, Genova_ **_Step into a world of endless possibilities, together let’s leave something for the future!_** At IIT, we are committed to advancing human-centered Science and Technology to address the most urgent societal challenges of our era. We foster excellence in both fundamental and applied research, spanning fields such as neuroscience and cognition, humanoid technologies and robotics, artificial intelligence, nanotechnology, and material sciences, offering a truly interdisciplinary scientific experience. Our approach integrates cutting-edge tools and technology, empowering researchers to push the limits of knowledge and innovation. With us, your curiosity will know no bounds. We are dedicated to providing equal employment opportunities and fostering diversity in all its forms, creating an inclusive environment. We value the unique experiences, knowledge, backgrounds, cultures, and perspectives of our people. By embracing diversity, we believe science can achieve its fullest potential. **THE ROLE** For recent relevant publications from our lab, see: - V. Kostic, P. Novelli, A. Maurer, C. Ciliberto, L. Rosasco, M. Pontil. Learning dynamical systems via Koopman operator regression in reproducing kernel hilbert spaces. NeurIPS 2022.
- V. Kostic, P. Novelli, R. Grazzi, K. Lounici, M. Pontil. Learning invariant representations of time-homogeneous stochastic dynamical systems. ICLR 2024.
- V. Kostic, K. Lounici, H. Halconruy, T. Devergne, M. Pontil. Learning the infinitesimal generator of stochastic diffusion processes, Submitted 2024
- T. Devergne, V. Kostic, M. Parrinello, M. Pontil. From biassed to unbiased dynamics: an infinitesimal generator approach. Submitted, 2024.




- P Novelli, L Bonati, M Pontil, M Parrinello. Characterizing metastable states with the help of machine learning
- Journal of Chemical Theory and Computation 18 (9), 5195-5202, 2022.
- J Falk, L Bonati, P Novelli, M Parrinello, M Pontil. Transfer learning for atomistic simulations using GNNs and kernel mean embeddings. NeurIPS, 2023.
- R Grazzi, M Pontil, S Salzo. Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start. Journal of Machine Learning Research 24 (167), 1-37 Within the team your main responsibilities will be: - to investigate open research problems in machine learning and computational physics,
- to write research papers and when appropriate opensource software to fully reproduce the results presented in the papers
- possibly, to be involved in coaching PhD students and interns. **ESSENTIAL REQUIREMENTS**
- A PhD in Applied Mathematics, Physics, Engineering, Computer Science, or related disciplines;
- Good record of publications in top tier conferences/journals in ML and related disciplines;
- A strong background on a least one of the following areas: - Machine Learning for dynamical systems and partial differential equations;
- Computational tools for numerical simulations, and a working knowledge of ML tools;
- Strong problem-solving attitude;
- Working knowledge of the ML ecosystem (Python, Pytorch, JAX, sklearn);
- The ability to properly report, organize and publish your research results;
- Good command of spoken and written English. **COMPENSATION PACKAGE**
- Competitive salary package for international standards;
- Private health care coverage;
- Wide range of staff discounts; **Application’s deadline**: 18th** October 2025

📌 Post-doc in Scientific Machine Learning (Genova)
🏢 Istituto Italiano Di Tecnologia
📍 Genova