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, research and innovation. As part of the Autonomous Pack project led by packaging tracking start-up GoodFloow, in collaboration with researchers at IMT, INRIA and IRCICA, and which aims to develop reusable
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collaborate closely with experimental partners (ICCF, IJL, IC2MP, and Syensqo) to validate computational predictions, ensuring the development of catalysts that are both highly active and stable under harsh
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neural population dynamics recorded by experimental partners - Collaborate with project partners - Participate in scientific activities of the team and scientific consortium - Study learning mechanisms and
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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focused on exploration and development of AI models of auditory perception, towards a broader goal of understanding how the brain predicts and learns from human communication sounds such as speech and music
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 8 days ago
) for the mathematical and computational aspects as well as by Mohammed Bendahmane (https://www.ens-lyon.fr/RDP/Morphogenese-florale/ ) (Inrae Lyon) for the biology. It will also be conducted in close collaboration with
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will work on this project under the supervision of Marie Kerjean, and may collaborate with other members of the ANR Diplo project. If the successful candidate wishes, the results obtained may be
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the research activities entrusted to the officer take place: This ANR project lies at the interface between statistical learning (mainly deep learning) and combinatorial optimization (mainly stochastic and
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Description Within the ANR HEBBIAN contract, the objective is to adapt bio-inspired Hebbian learning models recently proposed by one of the partners of this ANR (Frédéric Lavigne) in order to account for data