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
The position is within the Computational Statistics and Machine Learning (CSML) research unit at IIT. The successful candidate will be engaged in designing novel learning algorithms for numerical simulations of physical systems, with a focus on machine learning for dynamical systems. CSML has a strong focus on ML theory and algorithms, but also significant multidisciplinary interactions with other IIT groups in areas ranging from atomistic simulations, to neuroscience and robotics. We have also recently started international collaboration on Climate Modelling and Neuroscience.
The group hosts applied mathematicians, computer scientists, physicists and computer engineers, working together on both theory, algorithms and applications. Machine learning techniques, coupled with numerical simulations of physical systems have the potential to revolutionize the way in which science is conducted. Meeting this challenge requires a multi-disciplinary approach in which experts from different disciplines work together.
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),, 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, 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;
* Numerical optimization and its application to machine learning and deep learning;
* 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;
* Candidates from abroad or Italian citizens who have carried scientific research activity permanently abroad and meet specific requirements, may be entitled to a deduction from taxable income of up to 90% from 6 to 13 years.
Please submit your application using the online form and CV, a short research statement (max 2 pages) and names of two referees.
Application's deadline: 18th October 2025