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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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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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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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Description Within the ANR HEBBIAN contract, the objective is to adapt bio-inspired Hebbian learning models recently proposed by one of the partners of this ANR (Frédéric Lavigne) in order to account for data
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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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structure calculations, vibronic property simulations, and analyzing surface adsorption phenomena. Knowledge of machine learning potentials (e.g., GAP, ACE) or reactive force fields is a plus, as fallback
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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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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database
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responsibility of developing predictive tools based on machine learning for the analysis and interpretation of Raman vibrational spectra applied to battery materials. The successful candidate will design and