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significant computational component. We strongly recommend a background in machine learning and coding. Applicants with a background in areas such as computational neuroscience, reinforcement learning, or deep
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not only on its correctness, but also on when it is produced, making early detection a key requirement rather than a secondary performance criterion. Recent advances in deep learning have considerably
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costs and energy requirements of state-of-the-art deep learning models significantly, while democratizing them for a vast community of users, researchers, and practitioners. The task is to perform just
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the field of frugal or green AI TECHNICAL SPHERE You have a proven experience in frugal, green or low-resource AI Strong grasp of deep learning architectures (CNN, RNN, Transformers, LLMs). Experience in fine
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., Tacchetti, A., Bakker, M.A. et al. Scaffolding Cooperation in Human Groups with Deep Reinforcement Learning. Nat Hum Behav 7, 1787–1796 (2023). [22] Melnyk I., Mroueh Y ., Belgodere B., Rigotti M., Nitsure A
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 2 months ago
learning in humanoid robotics for industrial applications. In this project, our goal is to recruit one to two engineers to contribute to: the development of deep learning-based vision tools adapted
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. Akyildiz, “Deep kernel learning-based channel estimation in ultra-massive MIMO communications at 0.06-10 THz,” Proc. 2019 IEEE Globecom Workshops (GC Wkshps), 2019, pp. 1–6. [8] J. Tan and L. Dai, “Wideband
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and obsessive-compulsive disorders), and to optimise neuromodulation therapies such as deep brain stimulation. The team combines intracranial recordings and EEG, brain imaging, brain stimulation
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biophysics and deep reinforcement learning, and Médéric Argentina (INPHYNI, Université Côte d’Azur), an expert in nonlinear physics and learning in dynamical systems. By bridging artificial intelligence and
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, computational mechanics, computer science, applied mathematics or similar Strong experience with deep learning, e.g. PyTorch, JAX, TensorFlow, and probabilistic methods Familiarity with graph neural networks