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team at the Laboratoire d'Informatique de Grenoble (LIG). GetAlp conducts research in NLP, machine learning, evaluation, and interpretability. The project will be supervised by Maxime Peyrard (CNRS
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | 19 days ago
unified understanding of behavioral adaptation. The project aims to: Develop and analyze computational models that capture flexible, multi-timescale learning and adaptation in recurrent neural circuits
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on the plants Arabidopsis thaliana will generate maps of depolarization, retardance, dichroism, and optical axis azimuth, which will feed machine learning models developed by the project partners to identify
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and machine learning applied to data fusion and adapt them to the field of exoplanet characterization. They will develop and maintain the FORMOSA code in coordination with the team of students working
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anthropogenic factors using deep learning and vision transformer models, (2) Incorporating past factor trends for more realistic predictions under the non-equilibrium hypothesis, (3) Leveraging transfer learning
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models (transformers, diffusion models, GANs, etc.) * Quantum machine learning or tensor networks or quantum algorithms * Theoretical or applied aspects of quantum information Website for additional job
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l'institut du thorax, INSERM, CNRS, Nantes Université | Nantes, Pays de la Loire | France | 19 days ago
LevelPhD or equivalent Research FieldMathematicsEducation LevelPhD or equivalent Skills/Qualifications Must hold a Ph.D. degree in Mathematics / Computer science or Machine Learning. • Be able to work within
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reliability. · Understanding of hardware accelerators for AI and their operation. · Familiarity with machine learning workloads (e.g., CNNs). As this is a research position, it is necessary
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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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, decision-making and control using data, have been proposed. For control or management applications, reinforcement learning (RL/DRL), a branch of machine learning, is a promising solution that involves