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computational approaches to biological systems. Its core activity is the development of deep learning methods for protein design and optimization, with applications in biology and medicine. - activities: We
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 11 days ago
, Pengwenlong Gu, Cedric Adjih, Paul Muhlethaler, Ahmed Serhrouchni, "DS-IRSA: A Deep Reinforcement Learning and Sensing Based IRSA" in IEEE Global Communications Conference - GLOBECOM 2023, Kuala Lumpour
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deep neural activity, using methods that combine physics (waves in complex media) and computational approaches. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR8552-SYLGIG-011
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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria The position requires a PhD in machine learning, NLP, causality, or a related discipline, with a strong command of deep
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Machine/Deep learning and classification Knowledge of the Linux operating system for using a computing cluster Interest in transdisciplinarity and teamwork Autonomy and scientific rigor Website
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tools capable of integrating, modeling and interpreting this wealth of information. It is in this context that artificial intelligence (AI) approaches, particularly deep learning, offer considerable
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develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
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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 Deep learning models, and in particular large language
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trigger reconstruction architectures for future particle collider experiments, based on deep learning models distributed across multiple hardware processing stages. The mission of this position, based
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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
. To that aim, both Stein-based bilevel optimization, empirical Bayesian and unsupervised deep learning approaches will be considered. The recruited postdoc researcher will tackle both implementation challenges