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. Research Project: Current dose optimization strategies for oncology therapeutics rely primarily on clinician-reported adverse events (CTCAE) in exposure-response (ER) analyses, which may inadequately capture
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Details Title Postdoctoral Fellow in Energy System Optimization and Digitization School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position Description
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independent and collaborative research in optimization methods for power system operations and planning. Responsibilities include developing and analyzing optimization models for power system operations and
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Details Title Postdoctoral Fellow in Riemannian Optimization School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position Description A postdoctoral position is
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environment. Research Area and Environment. The successful candidate will work directly with the CMAI faculty. Broadly, the group’s activities lie at the interface of optimization and nonsmooth/nonconvex
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for understanding how AI-enabled control, optimization, and market design can support large-scale decarbonization, grid modernization, and the integration of distributed and flexible energy resources. Research topics
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the project directors and collaborators to develop data-driven and economically grounded frameworks for understanding how AI-enabled control, optimization, and market design can support large-scale
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optimization modeling. An ideal candidate would use their foundational knowledge of various modeling methods to develop and apply cutting-edge machine learning and data analysis techniques for assessing and
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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opportunity to field test and validate their methods using real-world systems. Postdoctoral fellows will work across the following research areas: Predictive machine learning Robust and stochastic optimization