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hydrological modelling, time-series analysis, and environmental data analysis. • Demonstrated experience in artificial intelligence, including machine learning and deep learning, applied to hydrological
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demonstrated experience in computer vision or analysis of pathology images. The appointees will participate in a multidisciplinary collaborative research project related to development of deep learning model
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materials modeling and mechanics, including molecular simulations, multiscale modeling, and/or machine‑learning‑based materials design Demonstrated publication record in peer‑reviewed journals relevant
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 hours ago
to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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analytical and interpretability frameworks to investigate the internal representations and decision-making processes of machine learning models. This includes developing and applying techniques such as feature
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recruiting an outstanding and ambitious postdoctoral researcher in computational biology to advance the integration and modeling of large-scale microscopy data using modern machine learning approaches
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areas: Generative AI Agentic AI Graph Representation Learning and Modeling Foundation Models Large Language Models Multimodal Learning Forecasting Models Basic Qualifications A Ph.D. or equivalent degree
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 3 days ago
learning and have strong mathematical skills. Knowledge of bandit models is a plus. LanguagesFRENCHLevelBasic LanguagesENGLISHLevelGood Additional Information Benefits Subsidized meals Partial reimbursement
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/ sonar, communications over dynamic channels, orthogonal time frequency space (OTFS) modulation, shared-spectrum / RF convergence, machine and deep learning (e.g. model-aided, convergence analyses
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new insights into the phenomena observed and enrich the databases required for deep learning methods. The neural networks currently being developed at LISTIC to detect and segment areas of movement in