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, with the longer-term objective of improving the prediction of fast-charging behaviour. Your project will be to build or adapt a machine-learning interatomic potential for lithiated graphite using density
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their 4th or 5th year of studies (M1, M2 or gap year) - Computer vision skills - Machine learning skills (deep learning, perception models, generative AI…) - Python proficiency in a deep learning framework
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-based, and machine-learning approaches - Identifying markers of intelligent, intentional, and malicious manipulations of detection systems - Developing detection and classification methods to distinguish
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investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable states, attractors
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» Computer engineering Researcher Profile First Stage Researcher (R1) Positions Other Positions Application Deadline 20 Oct 2026 - 17:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full
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, integrating statistical inference, machine learning, and population genetics. We will develop advanced computational methods to characterize the functioning of T- and B-cell repertoires. The goal is to build
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astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics and cosmology, in particular simulation-based
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develop a new generation of hybrid models combining large-scale machine learning with physical knowledge to represent interactions between mobile robots and their environment. The research will address
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with GREYC (UMR CNRS 6072), the computer science laboratory of Université de Caen Normandie, particularly in machine learning and graph-based approaches. Depending on the scientific questions addressed
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different dyadic motor coordination tasks. A range of neurophysiological measures (EEG, ECG and fNIRS) as well as behavioural measures will be recorded simultaneously from both partners. Machine-learning