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areas of data science such as machine learning, predictive modeling, decision-making under uncertainty, artificial intelligence, or computational statistics. This position is expected to strengthen
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, statistics, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology
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Are you inspired by the quest for new materials for solar cells, spintronics, and quantum technologies—and eager to accelerate their discovery with machine learning and materials theory? Are you
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other data scientists to advance precision medicine research. Your main tasks and responsibilities: Designing and leading analyses that apply state-of-the-art generative machine learning models (e.g
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fusion energy systems. This includes developing new machine learning models and software for scale-bridging and physics coupling in multiscale and multiphysics simulation codes for materials science and
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computer-aided material development, the finite element method and machine learning Expertise in the simulation of functional materials and mesoscopic systems Expertise and experience in the simulation
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contribute to the design and development of Machine Vision approaches for the quantitative analysis and phenotyping of agricultural systems: Training/Development of computational models for the quantitative
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machine learning pipeline to derive governing equations and boundary conditions for macroscopic crowd models from synthetic and real-world data. Close collaboration with a dedicated PhD student, who is
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Decision Support, the data warehouse team works with campus stakeholders to develop data models and intelligence delivery systems so the campus can achieve a wide range of strategic and tactical goals
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development of secure, scalable, and reproducible machine learning (ML) pipelines and software components that support AI tool deployment in a research setting. This role requires collaboration with data