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Requisition Id 16704 Overview: We are seeking a Postdoctoral Research Associate who will focus on AI-enabled plant ecophysiology to improve mechanistic understanding and predictions of ecosystem
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the area of precision network neurology, with a focus on understanding how risk factors contribute to brain aging. This research is situated within the framework of predictive, preventive, personalized, and
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producing well-structured, user-friendly, reproducible research code (e.g., packages, documented pipelines, open-source contributions, or similar). Written and oral communication skills, with the ability
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 13 hours ago
Organization National Energy Technology Laboratory (NETL) Reference Code NETL-Postdoc-2026-Goodman How to Apply A complete application consists of: An application, including academic history, work
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beyond traditional error estimators, creating physics-based adaptation algorithms that intelligently predict where refinement will be most beneficial for smarter, more efficient simulations. We seek
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environmental science is of supreme importance. Students entering the science classroom bring well-developed intuitive frameworks that help them understand, explain, and predict the world around them. These
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systems (proteins, enzymes, membranes, and complexes) Integrate AI/ML approaches with physics-based simulations to accelerate discovery and improve predictive fidelity Contribute to cross-scale modeling
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Cell 2024), 3) Deciphering pathogenic coding and non-coding variants linked to congenital heart disease in a cell type-specific manner (Cell 2022), and 4) Combining iPSC-derived cells and single-cell
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at the intersection of active transportation safety and AI (e.g., near-miss detection, predictive crash risk, multimodal exposure estimation). · Design, implement, and validate AI pipelines (computer vision
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and integration of multimodal neuroimaging, behavioral and clinical data, and building large-scale deep learning models for multimodal neuroimaging datasets to construct predictive network models in