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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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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 3 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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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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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
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collaborations. Why join us? By joining IRAMIS, you will become part of a dynamic and innovative research environment where you will have the opportunity to learn, grow and play a key role within a recognised
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collaborator visits. There are no teaching obligations, though teaching opportunities can be arranged if desired. The ideal candidate should have experience with machine learning, particularly in deep learning
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interface of machine learning, genomics, and scientific computing, contributing both methodological innovation and translational impact. Close collaboration with Helical-AI will ensure that developed models