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from backgrounds, including computational chemistry, bioinformatics, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental
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superlattices (twistronics). The role will focus on developing and applying theoretical models and computational quantum chemistry and machine learning methods to uncover novel properties and phenomena in low
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Engineering, Biomedical Engineering (Medical Informatics), or related areas. Recipient category: Masters, enrolled in the course: Degree courses: enrolled in doctorate. Non-conferring degrees courses: enrolled
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with an interdisciplinary team of statisticians, physicians, computer scientists, and health policy researchers. The successful candidate will lead development of variable importance measures – including
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surface science. Preferred Qualifications: Knowledge of kinetic modeling and/or machine learning interatomic potentials. Background Investigation Statement: Prior to hiring, the final candidate(s) must
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Electrical and Electronic Engineering, or related field. Research experience with Artificial Intelligence/Machine Learning/Large Language Model. Publication track record in a series of top tier conference
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, Geography, Civil Engineering, Computer Science, Mathematics, Physics or a related quantitative field. Skills and Knowledge: Knowledge of scientific computing, data assimilation, and machine learning
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and development activities in the fields of atomistic simulations, including density functional theory, machine learning, and molecular dynamics. The work involves theoretical and experimental research
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development. Familiarity with software development lifecycle including design and documentation of software architecture, testing/debugging skills, and version control. Experience applying machine learning
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, inverse problem, machine learning Expertise in the analysis of a wide range of geospatial and geodetic data sets Strong expertise in programming with Python or MATLAB As position requires stakeholder