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will combine first-principles atomistic simulations, scientific machine learning, reaction modeling, and integration of computational and experimental data. The associate will develop reproducible
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(JARVIS) for data-driven materials design" https://www.nature.com/articles/s41524-020-00440-1 2. https://jarvis.nist.gov/ 3. https://www.nist.gov/people/kamal-choudhary Keywords Machine learning
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, advanced fMRI data analysis and network modeling, integrating machine learning/AI methods for predictive modeling and large-scale data analysis, writing grants for external funding (with a preference for
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modeling. Qualifications You have (or soon will have) a MSc degree in computer science, machine learning, artificial intelligence, applied mathematics, physics, or a related discipline, meeting the
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modeling Experience with machine learning, hydrologic modeling, or energy systems modeling (e.g., ParFlow, WEPPCloud, agent-based models) Proficiency in scientific programming languages (e.g., Python
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engineering, mathematics, and statistics, are particularly encouraged to apply if they have experience in machine learning, systems modeling, optimization, or analysis of complex biological and clinical data
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und Stellenangebote > Academic staff > Postdoc Position on Physics-informed Generative Modeling and Multiscale Learning (m/w/d) Back to News Board Browse in News Postdoktorandenstelle im
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will combine first-principles atomistic simulations, scientific machine learning, reaction modeling, and integration of computational and experimental data. The associate will develop reproducible
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successful execution of R&D projects. Responsibilities include providing technical contributions as a software engineer for a wide range of projects involving machine learning (ML) and artificial
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candidates whose research leverages Artificial Intelligence (AI) and Machine Learning (ML) to advance process systems design, process safety and risk analysis, optimization, characterization, and