Postdoctoral researcher in Machine Learning and Dynamical Systems
Pubblicato il 21-10-2026 - IIT in Genova
Postdoctoral researcher in Machine Learning and Dynamical Systems
Contract and commitment: collaboration contract, for up to three years
Location: CHT Erzelli, Genoa, Italy
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
This position is based in the Computational Statistics and Machine Learning Unit, led by Massimiliano Pontil, and is funded by the ERC Advanced Grant project "LEO - Learning Evolution Operators" (Grant Agreement ID: 101267213). LEO studies the problem of learning the dynamics underlying temporal phenomena from observed trajectories.
Linear evolution operators play a central role, providing a powerful perspective for studying complex nonlinear systems through linear representations in suitable function spaces. Their spectra encode important physical properties of these systems, and a key objective of the project is to develop methods for learning these operators and estimating their spectra.
With a strong focus on the foundations of machine learning, LEO aims to develop efficient and reliable algorithms that ad