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Field
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, observations, a hierarchy of numerical models, and machine-learning methods to understand their formation, dynamics, and predictability. The successful candidate will have substantial freedom to develop
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, and traceability in accordance with the GDPR and the EU AI Act. Integrate predictive virtual biopsy models in distributed environments. Lead pilot experiments and monitor the convergence of federated
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emulation, active-comparator new-user designs, self-controlled case series and case-crossover studies, where appropriate. The role will also involve developing and validating clinical risk prediction models
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 19 days ago
scales, constraining uncertainties in Earth System Models (ESMs). Airborne Campaign Planning & Predictive Mapping: Conducting spatial scoping, observational gap analyses, predictive mapping of canopy
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. Desirable experience in AI-assisted image analysis, machine learning, computational modelling, or building predictive models from biological imaging or cell–material interaction datasets. Strong experience in
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Potentials (MLIP), machine learning (ML) predictive models and AI tools. Activities : Computer science implying ML and AI tools applied to material science Where to apply Website https://umontpellier.nous
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will also be integrated into the wider CENSEMAT research environment at Aarhus University, allowing computed models and predictions to be tested directly against advanced experimental characterisation
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 19 days ago
offers hands-on experience with operational satellite retrieval algorithms, physics-based forward modeling, and mission-scale validation practice, complementing the participant's background and advancing
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University of California, Los Angeles | Los Angeles, California | United States | about 10 hours ago
mechanisms associated with immune phenotypes, as well as development and application of machine-learning and statistical models for classification, prediction, feature selection, dimensionality reduction
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planetary atmospheres. The Section also initiates and manages a wide range of related modelling, software and hardware R&D activities. You are encouraged to visit the ESA website: https://www.esa.int/ Field(s