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, ensuring the preservation of geometric and topological properties. Conduct a literature review on representation learning (SSL, Geometric DL) applied to the sciences. Develop and implement tokenization
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the knowledge acquired during the PhD with team members and acquire new knowledge. - Engage with the Local team at LIPN and the wider national community working on proof theory, programming languages and
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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JET2SB team (PIs, postdocs and PhDs) from the Laboratoire de Physique des Plasmas (LPP, Paris, France) and the THEMIS team at the French-Spanish Laboratory for Astrophysics in Canarias (FSLAC, La Laguna
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Additional Information Eligibility criteria Training and experience: A PhD in Materials Science, Computational Chemistry, Physics, or a related field, with a strong background in DFT modeling and experience in
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correlations. - Analyse experimental data mixing both quantum optics and solid-state physics phenomena - Supervise PhD students and interns - Ensure an efficient collaboration and information sharing with
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria PhD in computer science, deep learning, or data science. Experience with multimodal models for biological data. Website
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the time of their application, a PhD degree in geology (including (bio)geochemistry, mineralogy/crystallography or experimental/isotope geochemistry/petrology), chemistry, physics or materials sciences. We