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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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missions operated by LATMOS. The postdoc will employ deep learning approaches using satellite data and ground stations. -Understanding the infrared data from the IASI mission and identifying the channels
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computational models and machine learning methods, as well as experience in repertoire data analysis. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8023-CLAMAR-001/Default.aspx Work
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The project aims at developing new learning methods
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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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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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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
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to apply Website https://emploi.cnrs.fr/Offres/CDD/UPR3228-SEVBOR-002/Default.aspx Requirements Research FieldPhysicsEducation LevelPhD or equivalent Research FieldPhysicsEducation LevelPhD
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to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR8023-ANTSAI-004/Default.aspx Requirements Research FieldPhysicsEducation LevelPhD or equivalent LanguagesFRENCHLevelBasic Research FieldPhysicsYears
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learning aspects, in order to connect the physical architecture, measurement protocols and classification performance. • Electrical and radio-frequency characterization of chains of magnetic tunnel junctions