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Responsibilities: Conduct programming and software development for data management. Design and implement machine learning models for optimizing data management. Conduct experiments and evaluations of the designed
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of machine learning, simulation-driven testing, and iterative calibration based on real-world datasets. Contribute to scholarly publications, technical documentation, and progress reports required by funding
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to develop and optimize scalable experimental protocols across diverse material families. This role is part of a multidisciplinary team integrating materials chemistry, machine learning, and autonomous
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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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relevant data science and machine learning tools. Able to work independently and comfortably with a team and external/international collaborators. Able to handle multiple tasks relevant to both project and
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writing/presentation Job Requirements PhD degree in an engineering field related to this project Experience in dynamic modeling, machine learning and optimization & controls Having basic knowledge in carbon
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Requirements: Preferably PhD in computer science or related field. Expertise in computer programming Knowledge in machine learning Proven research ability as evidenced through a portfolio of publications and/or
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Investigator (PI) or team lead with project management tasks. Job Requirements: PhD degree in Optimization, Artificial Intelligence, Transportation or Aerospace. Evidence of developing Machine Learning and
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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the system Development of inverse design frameworks using machine learning Development of full simulation for the chip-scale chirped-pulse amplification Use the full simulation to guide system fabrication