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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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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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learning, large-scale model optimization, and generalization. To explore scalable optimization methods for large-scale, distributed, and multi-node collaborative training. To conduct theoretical analysis
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project - “Oracle complexity bounds of first-order methods for nonsmooth optimization with nonconvex function constraints”. They will be required to conduct theoretical analysis, algorithm implementation
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research on shipping and port operational optimization, maritime decarbonization, data analytics, and the development and application of novel methodologies (including LLM-based prediction and optimization
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expertise in discrete choice modelling, statistical modeling, traffic simulation, machine learning, and optimization techniques. • Strong expertise in discrete choice modelling, statistical modelling, and
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, with expertise in dynamic/data-driven modeling, optimization, Aspen, COMSOL, or CFD b. Techno-economic analysis (TEA) & life-cycle assessment (LCA) for chemical, energy, or biomanufacturing processes
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, and optimize multimaterial LPBF processes, integrating in-situ monitoring, process parameters, material combinations, and resulting microstructural and structural outcomes. Design and optimize
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The successful candidate will work with Asst. Prof. Shen Shuting on combinatorial inference under a project on "Post-learning inference for near-optimal discrete structures". The main
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work at the interface of micro/nanofabrication, point-of-care (POC) development, and biomedical diagnostics. This role offers a unique opportunity to contribute to the design and optimization