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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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neural population dynamics recorded by experimental partners - Collaborate with project partners - Participate in scientific activities of the team and scientific consortium - Study learning mechanisms and
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collaborate closely with experimental partners (ICCF, IJL, IC2MP, and Syensqo) to validate computational predictions, ensuring the development of catalysts that are both highly active and stable under harsh
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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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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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will work on this project under the supervision of Marie Kerjean, and may collaborate with other members of the ANR Diplo project. If the successful candidate wishes, the results obtained may be
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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
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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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collaboration with the project's principal investigator and partner laboratories, the postdoctoral researcher recruited at Praxiling will be responsible for the linguistic analysis and modeling of the multimodal
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The