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substantial intellectual independence and opportunities to collaborate closely with machine learning scientists developing predictive models of cellular function. Machine learning experience is not required
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 12 hours ago
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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and AI-based approaches, including predictive modeling, stratification, explainable AI, and integrative multimodal analysis. The scholar will have opportunities to lead first-author publications
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between moisture uptake, glass transition temperature (Tg), and material performance, Development of predictive durability and service-life models for infrastructure applications including rehabilitation
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these operations, and then train transformer models to predict sequences of operations in unseen data. Please see Weindel, Borst, & van Maanen, eLife, 2025, Weindel, van Maanen, & Borst, Imaging Neuroscience, 2024
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infrastructure to capture and curate both experimental and simulation data and collaboratively build machine learning models to predict behavior of novel materials for memristor applications. This is an
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advanced statistical and computational analyses, including trial-level models, mixed-effects models, clustering or latent-profile analyses, dimensionality reduction, predictive modeling, cross-validation and
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, the mechanical state of the wall, or a defined combination of both. The resolved model will also predict the conditions under which the wall fails, with direct relevance to controlled, low-energy cell disruption
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strands. • Bespoke modelling of tumour metabolic function using 3D and 4D imaging data • Cancer patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics
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that combines data from different molecular levels (transcripts, proteins, metabolites), aided by in silico target prediction, will enable us to uncover putative target of the compound, which will then be