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-management algorithms for efficient and reliable long-duration operation. Implement hardware-in-the-loop (HIL) and real-time co-simulation for validation under realistic grid scenarios. Integrate battery
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executing the experiments Developing relevant algorithms for experimental data post-processing Mentor and work closely with multiple Ph.D. and M.S. students as well as four principal investigators from
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-cell and spatial-omics research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic
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capabilities, catastrophic forgetting, scaling laws, and other emergent physical phenomena. Building on this foundation, we aim to explore more effective training algorithms for Large Language Models (LLMs). (2
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” and “wet” lab workflows). You will be able to Design, develop and implement algorithms and systems based on foundation models, large language models and/or AI agents for automated scientific discovery
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digital twins using prediction-powered inference to enhance reliability assessment; The theoretical analysis and algorithmic development of methods rooted in statistical learning theory, multiple hypothesis
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-1,3,3,3-tetrafluoropropene (R1234ze(E)). The position combines mechanism building and validation with algorithm and database contributions to RMG, supported by electronic-structure data from literature, and
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responsibilities may include improvements to the geodetic early warning algorithm, G-FAST, and testing scenario earthquakes in different regions around the world. Minimum Education Required A Ph.D. is required, with
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strategy; participate in internal research sprints to explore, test and validate novel EO concepts, algorithms and workflows in a fast-paced, collaborative environment; engage with the innovation ecosystem
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Regularization. We aim to develop mathematical understanding of implicit regularisation properties in deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical