Post-doc in Machine Learning and Computational Chemistry

This postdoctoral project joins IIT's Atomistic Simulations unit, led by Michele Parrinello, and its Computational Statistics and Machine Learning unit, led by Massimiliano Pontil, to combine advanced numerical simulation with machine-learning theory and algorithms. The researcher will design physics-informed learning algorithms for atomistic simulations that are more efficient, interpretable and reliable than current methods, with applications that include problems relevant to the transition to a green economy.

The role suits machine-learning researchers interested in computational chemistry as well as computational physicists or chemists with working knowledge of machine-learning tools. Responsibilities include open research problems in machine learning and computational physics and chemistry, research papers, open-source software for reproducibility and possible coaching of PhD students and interns. The collaboration contract pays a gross annual salary of 35,000 to 42,000 euros.

PhD in physics, chemistry, computer science, applied mathematics or a related discipline; a good publication record in leading machine-learning and/or computational-chemistry venues; strong programming skills (Python, TensorFlow or similar); good spoken and written English. Applications include a CV, a research statement of up to two pages and two reference letters.