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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 3 months ago
(or surrogate models) are approximations of classical numerical solvers with a very low computational cost. They form the core of a digital twin. Using machine learning techniques to build these meta-models
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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: [email protected].
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(meta-)genomes Experience in the computer-assisted analysis of large biological datasets (e.g., using R, Python, and Bash/Linux environments) Very good written and spoken English skills Ability to work
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interest in data analysis, modelling, statistics, and machine learning. Experience in spatial data analysis (GIS), scientific programming (Python, R, or equivalent), or artificial intelligence will be
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an advantage: Good knowledge of mathematical optimization (e.g., mixed-integer linear programming, stochastic programming, meta-heuristics) Knowledge of machine learning and/or AI methods Experience with
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta