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e.g., ultra-cold gases of bosonic or fermionic atoms, machine learning technologies and quantum computing. At the same time, we work in close connection with IJCLab experimentalists, particularly
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out with the consortium to transfer them to the Angers site # Preclinical evaluation of therapeutic drugs acting on the mitochondrial thermostat with the consortium Where to apply E-mail job-ref
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on the development of advanced artificial intelligence and machine learning methods for genome interpretation, with a particular emphasis on modeling the relationship between genetic variation and phenotypic outcomes
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and economy that respect people and their environment. We are looking for our future postdoctoral researcher in optimization for statistical learning to join the Image, Data, Signal (IDS) department
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support machine learning applications for analyzing electron microscopy images of nanoalloys. Model interactions between nanoalloys and carbon substrates to reflect experimental conditions, incorporating
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 1 month ago
skills Specific Requirements The candidate must hold a PhD in machine learning and have strong mathematical skills. Knowledge of bandit models is a plus. LanguagesFRENCHLevelBasic LanguagesENGLISHLevelGood
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experienced candidates could also be considered. The candidate should have a good command of English. While knowledge of French is not mandatory at start, the postdoc would be expected to learn spoken and
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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform
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. Skills in computational modelling or machine learning applied to brain signals are an asset. We are looking for a highly motivated, rigorous and curious researcher who is ready to invest themselves in a
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on the ERC-funded project ‘Understanding the Consequences of Major Health Crises for Education: Learning from the COVID-19 Pandemic (LEARN)’. The LEARN project: Health crises, natural disasters, and violent