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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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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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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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· Strong background in machine learning, deep learning, applied mathematics, or nonlinear dynamical systems. · Experience in modeling biological systems, computational biology, or related fields is a
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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 | 10 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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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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and distributed control intelligence that can be applied to solve these problems through the application of machine learning, intelligent optimization techniques, automated fault detections and
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/interventions, and clinical diagnoses. The post would be suitable for applicants with general interests in AI, machine learning, large language models, foundation models, signal processing, computational