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on developing deep learning methods for the reconstruction and physical analysis of ATLAS experiment data. The selected candidate will develop innovative analysis methods for the reconstruction and physical
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in privacy preserving machine learning (ML) within the SSF-ML-DH project, under the supervision of Olivier Cappé (CNRS, DI ENS) and Jamal Atif (Ecole Polytechnique, CMAP). Funding is available for two
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oxides, we will first develop and benchmark machine-learning interaction potentials of increasing complexity. Subsequently, we will deploy a combination of brute-force and rare-event sampling to isolate
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representative tests in small-scale batteries. A multidisciplinary team is involved in the project with much excellence and expertise in the topic. An example of a recent related research can be found here: https
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of the multidisciplinary TrAI-SOC project, coordinated in collaboration with UGA's CERAG laboratory. SocSIM-K: https://lig-socsim-k.imag.fr/ LIG: https://www.liglab.fr/en Activities Design the game and define all of its
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. Unsupervised machine learning approaches will be used to identifiy key dimensions of circadian rhythm associated with dementia subtypes. This requires a very good level in statistics and R programing as
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on deep learning coupled with molecular simulations for the investigation of slow variables and transition pathways describing large conformational changes and reactive processes in complex biological
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and economy that respect people and their environment. We are looking for our next postdoctoral researcher in computer graphics and machine learning to join the Image, Data and Signal (IDS) department
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develop and apply cryo-ET on microalgae from laboratory cultures and field samples. At LPCV, you will contribute to the research themes of the Photosymbiosis team led by Johan Decelle (https
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Lazuli collaborations. The recruited person will join the Cosmology team at IP2I and will work under the direct supervision of Dr. Mickael Rigault. Where to apply Website https://emploi.cnrs.fr/Offres/CDD