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Postdoctoral Associate in Optimization Modeling The School of Civil and Environmental Engineering at Cornell University seeks a postdoctoral associate. The positions can be remote (but the home location needs
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planning and operations, and the interaction with other infrastructures. Some of these tools will require the development of underlying optimization methodologies and analytics frameworks. The successful
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Postdoctoral Associate in Optimization Modeling The School of Civil and Environmental Engineering at Cornell University seeks a postdoctoral associate. The positions can be remote (but the home
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optimization technologies are revolutionizing the way power grid is operated and planned. CEEESA is seeking talented and motivated researchers to enhance its capability in solving energy challenges using
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We invite applications for a Postdoctoral Appointee to contribute to a growing research program in process systems modeling and optimization for clean energy, critical materials, and advanced
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Posting Details Position Information Posting Number PG191517PD Position Number 00111548 Position Type Post Doc FLSA Departmental Information Department ID 120401 - Landscape Architecture Department 120401 - Landscape Architecture Job City & State Raleigh, NC
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About the Opportunity The Postdoctoral Research Associate advances research in mathematical modeling, optimization, and stochastic analysis for large-scale and distributed systems. The role focuses
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applications for a Postdoctoral Researcher position. The selected candidate will undertake both collaborative and self-directed research on mathematical modeling, optimization and simulation models/algorithms
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). Solid understanding of optimization and regularization methods for training complex neural networks. Practical knowledge of interpretability methods to ensure ML outputs are meaningful in scientific
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. Proficiency in Python programming and major ML/DL frameworks (e.g., PyTorch, TensorFlow). Solid understanding of optimization and regularization methods for training complex neural networks. Practical knowledge