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are looking for a postdoctoral researcher in the field of image generation algorithms (specializing in Deep Learning) for a 12-month position. The work will take place within the IMAGE research team
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, involving expertise in optics, electronics, image and data processing, chemistry, and biology. With the support of several European funding programs, the team is building a data science and machine learning
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conceptual DFT (linear response function, Fukui functions) or QTAIM theory (delocalization index), and their validation on a set of compounds known from the literature - interfacing a MLIP (Machine-Learned
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multidisciplinary experience. Knowledge in applied computer science, particularly in machine learning; in fluid mechanics, especially in hydrodynamics; and in electronics, particularly in instrumentation and
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and AI to efficiently design safe systems. This is a postdoctoral position in the fields of AI planning, reinforcement learning (RL), and formal methods. The position is initially funded for 12 months
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a team More specifically: - For mission 1: knowledge of signal and image processing, machine learning (PyTorch or TensorFlow + NumPy/SciPy), statistical processing & data and results visualisation
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nervous system to modulate future aversive/nociceptive experiences (Merabet et al., in preparation). Notably, this effect participates in how flies learn the relative aversive value between aversive
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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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” focusing on the effect of a fluctuating environment on the collective dynamics of self-propelled agents, a numerical part on “reinforcement learning” focusing on optimizing communication between agents in a
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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in