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Biophysical and Biomedical Measurement Group - NIST | Gaithersburg, Maryland | United States | 13 days ago
processing, internal standards/calibration, and uncertainty quantification. - Experience with spectroscopy, chromatography, electrophoresis, fluorescence methods, or complementary molecular characterization
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is to obtain imaging methods that are more robust and computationally efficient, while also providing a natural framework for uncertainty quantification and experimental design. A central question is
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Qualifications Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification
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inference, counterfactual explanations, or uncertainty quantification in deep learning Evidence of high quality scientific writing, publications, a strong master's thesis, research software, or relevant open
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on frontier models like Large Language Models (LLMs) and multimodal foundation models. This includes topics such as safety alignment, adversarial training, jailbreaking, uncertainty quantification, and AI
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, formal verification, proof assistants, automated proof search, and AI-guided mathematical discovery; neural operators, physics-informed learning, inverse problems, uncertainty quantification, and rigorous
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-informed learning, inverse problems, uncertainty quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning, manifold learning, equivariant
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estimation of 3D coronary hemodynamics (velocity, pressure, and wall shear stress fields); design computational pipelines that integrate image-based anatomy, blood flow physics, and uncertainty quantification
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) Familiarity with uncertainty quantification, railway or infrastructure asset management, or geographic information systems is considered an advantage Proficiency in written and spoken English is required, and
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states are unobserved. Purely data-driven models offer flexibility, but often ignore known biology and provide limited insight into uncertainty and mechanisms. These challenges motivate a broader Biology