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The School of Chemistry, Chemical Engineering and Biotechnology is seeking for a full-time Research Fellow. Key Responsibilities: The Research Fellow will work together with R&D team to conduct
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to the development of high-performance, multimodal sensor systems with strong application potential in digital healthcare, robotics, and smart environments. The successful candidate will be an integral part of a
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Responsibilities: Conduct research on the design and analysis of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop algorithms and prototypes
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framework on how to build up a generic framework to use learning-assisted approach to solve various optimization problems Develop mathematical modeling framework to find the optimal operation strategy Conduct
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engineering strategies to enhance production yields of target chemicals. Conduct lab-scale fermentation, analytics, and process optimization. Publish research findings in high-impact journals and present
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for the first time. This will allow us to work out the best vital-sign-based early warning score. It will also allow us to build an electronic maternal early warning score, harnessing the power of artificial
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uses. To investigate feature-level just-noticeable difference modelling for machines to facilitate assessment and optimization. To formulate a comprehensive visual feature codec for machine uses
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underserved populations. We conduct groundbreaking research that advances treatment, we educate tomorrow's physician/researchers, and we work with amazing partners, including other Harvard Medical School
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electronically when significant events happen. We will then develop a large multi-centre maternity routine dataset for the first time. This will allow us to work out the best vital-sign-based early warning score
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Responsibilities: Lead the design, implementation, and validation of data-driven Digital Twin (DT) models to simulate and forecast dynamic energy demands and renewable generation patterns within shipyard operations