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• PhD degree in STEM. • Proficiency in at least one mainstream deep learning framework (PyTorch or JAX) • Fundamental knowledge of physics, chemistry, and materials science. Research
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Apply Now How to Apply Application process Please visit the CHEAR Fellowship page (CHEAR Fellowship | CHEAR ) Application materials include: Fellowship application form An up-to-date curriculum
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-destructive testing (NDT) techniques for structural materials is desirable. Experience with structural modelling and analysis tools (e.g., finite element modelling, structural simulation) is desirable. Good
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 2 months ago
genetic data Multimodal machine learning for biological discovery Translational genomics and risk modeling The fellow will work in an environment that emphasizes methodological innovation, statistical rigor
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, or a related field Strong background in computational modelling of surface chemistry and oxide materials Experience in developing and training interatomic machine learning potentials Experience in
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affiliated with Columbia University. Duties and Responsibilities: Construct air pollution and meteorological data prediction models using machine-learning based methods Conduct epidemiologic analyses studying
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(AI4S) research, to develop AI software powered by large language models (LLMs) for process engineering and materials discovery, to mentor students, and to contribute to grant writing and publications
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transition to Net Zero. You will be responsible for developing novel numerical methods to model continuum problems in a two-liquid/granular medium and for interpreting the results physically. These models will
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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Develop theoretical models of topological materials to understand & interpret experimental data Leverage nanofabrication facilities & collaborate with other leading research groups at the School