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to identify and model the most efficient catalytic sites on NDs using advanced Density Functional Theory (DFT) calculations. The project seeks to revolutionize the design of ND-based catalysts by controlling
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. The Research Associate will take on a significant role in developing and applying simulation and machine learning models for Asian leafy vegetables to predict growth, transpiration, resource use, and yield under
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design (especially membrane proteins or ion channels), learned-model–assisted protein design workflows (e.g., structure prediction, inverse folding/sequence design, protein language-model–based sequence
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background in zoonotic and vector-borne disease to join our CDC-funded project team to strengthen partnerships with public health in the Ohio River Valley and develop predictive models and hazard maps that
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-driven frameworks for multi-scale modeling, multi-objective optimization, and predictive control of complex chemical and biochemical processes. The research will contribute to next-generation smart
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Modeling and outputs. Implements Strategic Modeling governance, data-quality controls, and version-management processes. Leadership, Team Management, and Staff Development Supervises two Strategic Budgeting
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high-temperature plasmas remains a major challenge. This project will investigate the physics of turbulence at the edge of tokamak plasmas, developing models to better understand and predict complex plasma behaviour
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an integrated framework for co-design and adaptive operation of electrical networks. DynConGrid develops real-time Model Predictive Control (MPC) for congestion management by reconfiguring topology, curtailing
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oversight for predictive, prescriptive, and exploratory analytics initiatives across multiple concurrent projects, including risk stratification, response-to-therapy modeling, and outcome prediction. Create
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settings. The ultimate goal is to enable early, systematic, and robust screening of children at risk of neurodevelopmental disorders. Deep learning models typically produce point predictions, whose