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the Job related to staff position within a Research Infrastructure? No Offer Description The LIG brings together nearly 450 researchers, lecturers, PhD students and research support staff. The position
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foundations of feature learning in neural networks. Engage in the life of the CSD, including participating at the weekly seminars. Present results internally and at international venues; Qualifications PhD in
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
Framework Programme? Not funded by a EU programme Reference Number 2025-09322 Is the Job related to staff position within a Research Infrastructure? No Offer Description Context. This PhD thesis is part of
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(formulation, algorithms, applications in structural mechanics), HPC computing, reduced-order modelling, machine learning, Vibrations and structural dynamics, architected materials, Additive manufacturing
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for such applications. To respond to these challenges, this project aims to investigate automated decision making based on machine learning. The candidate (H/F) will propose and validate centralized as
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that interaction represents the foundation of active learning and fosters acquisition and retention of knowledge, as opposed to passive reception in traditional teaching. Some benefits of MR are now well established
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skills (one or more of the following strongly desired) Exploratory analysis of massive datasets (machine learning methods) Spatial data analysis and Geographic Information Systems (GIS) Forecasting and
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algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation
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. The candidate must be able to communicate in English (oral and written). The knowledge of the French language is not required. The candidate must have a strong interest in machine learning. Skills in
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properties changes. - The demonstration of the tear detection with machine learning classification applied directly on S-parameters of the MWI system without solving the inverse problem. The objective