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modelling, multimodal neuro-imaging and physics-informed machine learning to improve assessment of glioblastoma treatment response. The candidate will also be expected to contribute to the formulation and
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accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models, including large
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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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/ThreeBodyTB.jl), cluster expansion, classical potential development, and machine learning. In addition to work on specific problems, I work on developing new first principles-based modeling approaches, including
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Science, Computer Science, Data Science, Neuroscience, or a related field by the start date. Demonstrated expertise in computational modeling of human behavior or computer vision / machine learning
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, in close collaboration with the machine learning group at the Department of Informatics, both at University of Oslo. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | 12 days ago
-scale (~10’s of km2) permafrost thermo-hydrological hybrid twin, to be coupled with state-of-the-art freezing/thawing soil mechanics machine learning-based surrogate models (Richa et al., 2024, Tristani
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models