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therapeutic targets. Develops and applies machine learning and statistical modeling approaches for biomarker discovery, classification, prediction, and patient stratification. Develops algorithms and
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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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project. The position will provide core expertise in physics-based environmental and ocean modeling, with emphasis on numerical simulation and prediction of ocean conditions such as currents, tides, waves
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the design and operation of next-generation thermal systems, developing strategies to model, optimize, predict, and autonomously control thermal systems behavior in complex systems. The candidate will
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transport models to assess the production and transport of in-vessel impurities in MPEX operating scenarios. Perform predictive modeling to inform the design of new MPEX target geometries, with a focus to
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convection. The objective is to develop reduced but realistic models of cumulus life cycles that may be applied toward cumulus parameterization and/or machine-learning algorithms for predicting short-term
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optimization models. Investigate how tabular foundation models can support energy-system modelling and optimization, including applications such as prediction, surrogate modelling, uncertainty quantification
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Sciences Location Bloomington Position Summary Position Overview The Postdoctoral Researcher will join an NSF-funded project developing and testing computational models of how decisions evolve over time. The
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Landscape analysis and modeling, with a focus on training and application of predictive machine learning tools. This researcher will synthesize regional-scale remote sensing imagery and ecological monitoring
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National Aeronautics and Space Administration (NASA) | Merritt Island, Florida | United States | 1 day ago
physiological responses. By incorporating these imaging-derived traits into genomic prediction workflows, the participant will explore how high-throughput phenotyping strengthens selection models. While