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for the conductive state under different conditions of use will be targeted. WP4 – Functional optimization: Development of the functional optimization of the DTIS, using predictive modelling based on climate forecast
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the University of Southampton. The project aims to develop innovative computational approaches to predict which vertebrate species are most sensitive to endocrine-disrupting chemicals and to identify the molecular
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to such inhomogeneous fields. Mathematical modelling combining physical and biological aspects offers a tool to tackle this problem. In the project, a predictive model should be enhanced and validated for cellular and
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management. Artificial Intelligence & Machine Learning: Predictive risk modeling for falls, hospitalization, and cognitive decline; Natural Language Processing (NLP) for remote cognitive assessment; image
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generation, targeted atomistic simulations, structural descriptor extraction, and predictive models. The work will aim to establish links between local pore geometry, structural disorder, sodium adsorption
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machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in industrial furnaces
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dementia or Alzheimer's disease research. (Preferred) Experience developing disease identification algorithms or prediction models. (Preferred) Knowledge of longitudinal data analysis, survival analysis
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 5 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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simulation tools for predicting module coalignment performance. The participant may also engage in commissioning activities for X-ray detectors and participate in operating a hard X-ray reflectometer
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validating uncertainty-aware AI models for real-time crash risk prediction with guaranteed confidence bounds, interpretability, and fairness-by-design, using a blend of centralized and federated learning