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learning applied to dynamic systems; Proficiency in key machine learning libraries (PyTorch, JAX, etc.); Mastery of Python and the software ecosystem for scientific data analysis and management (NumPy
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Starrydata2). The work will include the implementation of machine learning models (neural networks, random forests, SISSO), generative approaches for predicting crystal structures, the use of machine learning
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the laboratory at the Géosciences Rennes site (Rendal platform); - chronological modelling using software developed in R language. This work will be carried out in close collaboration with other PhD students in
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analysis) to compare brain responses with predictions of computational models (deep neural networks developed by the NASCE team). The objectives include assessing how the brain segments, groups
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. Familiarity with single-cell data and experience with existing single-cell methods and software would represent a strong advantage. Excellent communication skills and team spirit, and an ability to work in