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progression modelling, exploiting advances in deep feature learning and uncertainty quantification to support the Bayesian framework, as well as implementation of computational models of neurodegeneration
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multimodal omics data. The project will focus on multimodal representation learning, uncertainty quantification, and interpretable and biologically plausible modeling, with the goal of building foundational AI
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science, uncertainty quantification, and optimization/operations research to evaluate and recommend carbon reduction strategies under cost, schedule, and quality constraints. o Create decision-support
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human and natural systems as well as intrinsic variability. The need to translate to variables and scales relevant for stakeholders with appropriate uncertainty quantification requires physics-guided AI
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characterization, and uncertainty calculation. Interest in topics related to gas emissions and the development of reference materials, as well as the ability to work rigorously in a laboratory environment. Must
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programme Reference Number AE2025-0503 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0503
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information science, statistics and probability, and uncertainty quantification, for applications with scientific and national priority. ORNL’s mathematics research efforts provide fundamental mathematical methods and