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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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· 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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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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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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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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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | 6 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 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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None Additional Preferred Experience working in one or more of the following areas: Longitudinal data analysis Predictive modeling/machine learning models Biostatistics / epidemiological modeling
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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research threads in Computer Vision and Machine Learning : Improving and creating state-of-the-art foundation models to be able to enhance both performance and computational efficiency; Design world models