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thermomechanical processing and develop computational models to predict mechanical properties, their evolution, and formability. Through August 2026, contribute to the DOE ARDAP research project by evaluating
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. - Conduct high-throughput serum proteomic analyses and integrate molecular datasets. - Validate candidate biomarkers in independent cohorts. WP3.2 – Integrated predictive modeling: - Develop integrative multi
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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 5 hours ago
traditional predictive attempts and limits the availability of training data for high-resolution atmospheric and hydrological models. This limitation is compounded by the fact that many atmospheric reanalysis
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to identify those most at risk from extreme heat, as well as offering personalized adaptation advice --- translating rich multi-modal data into interpretable, scalable prediction and advising models. ICARUS
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 5 hours ago
carbon-cycle modeling. The project will build a unified modeling framework that uses GEDI LiDAR and Landsat/HLS data to train deep learning models capable of predicting forest structure variables such as
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:this project pioneers a new paradigm of General Genome Interpretation (GenGI) models by combining DNA Large Language Models (DLLMs) with Deep Neural Networks to predict human phenotypes directly from Whole Exome
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teams. Unit URL https://imci.uidaho.edu/ Position Qualifications Required Experience Experience with statistical or predictive modeling as demonstrated by publications in the field Evidence of strong
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implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment
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Modeling Core is home to a consortium of postdoctoral fellows who provide modeling expertise for a wide range of projects as integral members of those research teams. Unit URL https://imci.uidaho.edu
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, the postdoctoral researcher will be responsible for contributing to the development of advanced methodologies for predicting crystal structures (CSP) based solely on their chemical composition and atomistic modeling