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structure, atomic orbits, and model applicability domains Train and benchmark large-scale MLFF models on diverse molecular and materials datasets Integrate uncertainty estimates into active learning pipelines
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engineering practices for machine learning Tabular machine learning Large language models on structured and semi-structured data Research Associate Role: Under the direction of their supervisor, the candidate
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graphs and related structures, limit theorems, stochastic calculus and applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department
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by integrating large-scale single-cell foundation models with structured biological knowledge encoded in genomic graphs. The project will also deliver efficient algorithms to train these models under