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molecular docking, molecular dynamics and free-energy methods (MD/FEP), machine learning for molecular design, and protein–ligand modelling. Experience bridging computational and experimental groups, and the
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-scale vision/language/action models) can guide: World models for learning predictive representations of system dynamics Model Predictive Control (MPC) for robust decision-making under uncertainty Robotic
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et d'assurer la stabilité des performances dans le temps. Cette thèse s'inscrit dans le cadre de l'apprentissage continu, un domaine émergent du machine learning, qui vise à concevoir des modèles
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research units: GEPEA, IRISA, LATIM, LABSTICC, LS2N and SUBATECH. The proposed thesis is part of the research activities of the team MOTEL (Models and Tools for Enhanced Learning) of the Lab-STICC laboratory
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, seeks to recruit a junior research scientist to develop AI-enabled healthcare applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based
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will produce a dataset on social interactions aimed at training machine learning models for human-robot interactions. Robot decision using internal simulations : As mentioned at the beginning of
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, ML models, and data processes This role requires advanced data science and machine learning expertise, proficiency with Python ML libraries, strong SQL programming skills, experience with data pipeline
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/deploying deep learning models and machine learning applications. Computer skills: Python (PyTorch, TensorFlow), databases (MySQL), 3D Slicer, ITK-SNAP, OpenCarp. Previous experience in research activity in
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. The successful applicant will develop a predictive pipeline using atomistic modeling and machine learning to identify optimal "seeds" for directing crystal growth, followed by rigorous experimental testing
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demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands‑on experience with common machine learning / deep learning