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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 6 days ago
Python and good analytical skills. A good background in probability/statistics and deep learning is expected. Knowledge of differential privacy and/or fairness is a plus, but not necessary. The candidate
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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agents. Your research will explore how reinforcement learning, multi-agent cooperation and generative worldmodels can deliver adaptive strategies that thrive amid volatile, multi-asset markets, micro
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Your Job: Reinforcement Learning (RL) is a versatile and powerful tool for control, but often data-inefficient, requiring numerous updates and non-local information such as replay buffers and batch
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, particularly deep learning and optimization methods Excellent coding skills, particularly in Python and machine learning frameworks (PyTorch or Jax) The ability for creative and analytical thinking across
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degree (or equivalent) in Data Science, Computational Biology, Bioinformatics, Computer Science, Physics or a related field Solid programming skills and knowledge in deep learning, statistical modelling
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. (or equivalent) in Computer Science or a related discipline ML expertise: You have strong programming and deep learning experience (e.g., PyTorch, TensorFlow), backed by a substantial project (thesis, paper, etc
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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Description We offer a deep immersion in bio-based energy technologies; the candidate will learn and live the translational perspective of designing biomaterials for sustainable energy-related applications