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apply machine learning and deep learning models (e.g., graph neural networks, generative models, transfer learning) for materials property prediction, interpretation, and inverse design. Perform high
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teaching and research covering a wide spectrum of subjects within mathematics, mechanics and statistics. The research is on theory, methods and applications. The areas represented include: fluid mechanics
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trials, and treatment guidelines, and transform these into structured knowledge graphs encoding relationships among histotypes, biomarkers, therapies, and outcomes. Assess the accuracy, completeness, and
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economics, management, mathematics, or a related field. Specific requirements: Candidates must also have knowledge/research experience in the following areas: Quantitative methods (e.g., game theory
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background in at least one of the following areas: renewable energy sources, power systems integration, energy technology management, or smart grids. Experience in development of mathematical meta-models
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Position Description The Onnela Lab in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health is seeking candidates with a Ph.D. in biostatistics, applied mathematics
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science, mathematics, physics and sport science. This mix of traditional and modern subjects encourages and inspires future innovators. In the Department of Engineering we deliver research and teaching in
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university, with expertise in one of the following disciplines: Biological Sciences, Medicine, Pharmacy, Chemistry, Physics, Mathematics, Computer Science & Electronics, Materials Science & Engineering
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, mineralogy, mineral physics, paleomagnetism, seismology, and planetary sciences. We welcome methodological approaches that span field observations, lab-based science, theory, and computation. The Department
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extreme weather to cybersecurity threats. Working within the LDTRC, you will undertake a range of research tasks, including: 1) Defining the ontology and knowledge graph architecture for a scalable digital