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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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RAP opportunity at National Institute of Standards and Technology NIST Modeling Complex Microstructures Location Information Technology Laboratory, Applied and Computational Mathematics Division
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Dynamic Atomistic Predictions of Crystalline, Crystal Defect and Liquid Metal Properties NIST only participates in the February and August reviews. Classical interatomic potentials provide a means
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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 13 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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experimentally, followed by further model improvements, and implementation or design of a robust workflow and predictive design tool. Where to apply Website https://www.academictransfer.com/en/jobs/359149/engd
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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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protein structural insight with hands‑on ML development: adapting and applying state‑of‑the‑art structure prediction and design frameworks, training/fine‑tuning models, and running scalable computational
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PhD MSCA - Acoustic and Ultrasound-based Predictive Maintenance Systems for Industrial Equipment Power converters are essential in numerous applications such as industry, photovoltaic systems
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state‑of‑the‑art structure prediction and design frameworks, training/fine‑tuning models, and running scalable computational campaigns. Key responsibilities Design and execute in silico protein and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 13 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