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, or materials informatics. Familiarity with explainable AI or counterfactual explanation methods. Experience with molecular dynamics data, graph neural networks, or multi-component system modelling. Track record
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skills: Good knowledge of ML/AI based techniques to develop fast surrogates (deep neural networks) and capability to develop own efficient model learning schemes (deep learning techniques, representation
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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of this interdisciplinary project is to study the neural basis of language development. The candidate will be hosted at the Maresca Lab, which is part of Imaging Physics department of TU Delft. The lab currently consists
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mentoring of members of our research network, as well as outreach activities, all generally related to your research topic though not exclusively. You are encouraged to visit the ESA website: https