Post-doc in Machine Learning and Computational Chemistry
Pubblicato il 19-10-2026 - Istituto Italiano Di Tecnologia in Genova
Post-doc in Machine Learning and Computational Chemistry Italy
Commitment & contract: collaboration contract
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 Numerical simulations of physical systems coupled with machine learning techniques have the potential to revolutionize the way in which science is conducted. Meeting this challenge requires a multi-disciplinary approach in which experts from physics and machine learning work together. To this end, we are seeking a talented and motivated postdoctoral researcher to join a collaborative project between the Atomistic Simulations and Computational Statistics and Machine Learning research units at IIT, led by Profs. Michele Parrinello and Massimiliano Pontil, respectively.
The goal of the project is to combine the expertise of the two units, that is to say advanced numerical simulations and machine learning theory and algorithms. The successful candidate will be engaged in designing novel physics informed learning algorithms for atomistic simulations, which are more efficient, interpreta