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, France Subject Area: Machine Learning / Machine Learning Appl Deadline: 2025/12/12 11:59PM ** (posted 2025/10/21, listed until 2026/04/21) Position Description: Apply Position Description Applications
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and economy that respect people and their environment. We are looking for our future postdoctoral researcher in model-based reinforcement learning to join the Computer Science and Networks (INFRES
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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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23 Sep 2025 Job Information Organisation/Company Université Grenoble Alpes Research Field Computer science » Other Researcher Profile Recognised Researcher (R2) Country France Application Deadline 13 Oct 2025 - 01:21 (UTC) Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Nov...
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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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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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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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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 1 month ago
, CIRAD AMAP, CIRAD PHIM, CIRAD PBVMT, INRAE ePhytia, INRAE IGEPP, INRAE LISAH, IRD EGCE, IRD IEES, Univ. Paris Saclay, TelaBotanica). This is a postdoctoral position in Machine Learning, more
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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