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, particularly radionuclides, on a continental scale. The aim is to develop a new class of inverse Bayesian models, STE-EU-SCALE, combining innovative forward dispersion models, machine learning techniques, and
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methods and potential application of machine learning techniques. Good command of English (B2/C1). The candidate will be responsible for carrying out tasks related to the analysis of experimental data
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. Essential qualifications: PhD in Computer Science, Robotics, Machine Learning, or related fields. Programming skills in Python/C++ and experience with ML frameworks (e.g. PyTorch, TensorFlow, JAX). Strong
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is modelling and optimization using molecular dynamics and machine learning. The position is part of a team looking at these fascinating systems from their fundamental properties and making them in