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learning, particularly flow-matching generative models and protein language models. The research will focus on designing efficient generative models able to produce realistic conformational ensembles while
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for projections. This project aims to explore the coupling of the ocean and ice-sheet model components via a machine learning emulator of ice-shelf cavity circulation. While the ultimate goal of the project is to
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, machine learning, explainable artificial intelligence (XAI), digital twins, and integrated data-model approaches. • Study of the frugality of the developed approaches by reducing the requirements
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methodological developments along the chosen direction (inference, active-matter theory, or machine learning). ◦ Algorithmic implementation and validation of the developed tools. 5. Validation on model systems
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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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modern machine-learning techniques, will be exploited to improve the discrimination between the different polarization states. The analysis will use the complete Run 2 and Run 3 datasets collected by
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computational fluid dynamics. • Experience in modeling, uncertainty quantification, or statistical methods. • Experience in data science or machine learning is considered an asset. • Experience with high
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are seeking a candidate holding a Master 2 degree in computational biophysics, structural bioinformatics, or a related field. Knowledge of statistical mechanics and/or machine learning would be an asset
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to align neuromorphic algorithms with the physical constraints of the target hardware. This hardware–software co design effort will involve: • Deepening and extending NSS-related machine learning and