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: Develop novel machine learning theories and techniques for analyzing noisy time-series data, with a particular focus on seismic signals Perform uncertainty quantification in time-series analysis to assess
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updates to principal investigator and funding agency Report writing/presentation Job Requirements PhD degree in an engineering field related to this project Experience in dynamic modeling, machine learning
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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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requirements: PhD Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field, obtained within the last five year Research Experience in one or more of the following
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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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will conduct the lab experiment for RAS system for pollution control in recycled water in aquaculture system. He/she will also use machine learning tools to predict and optimize the RAS system. Job
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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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diffusion models using path integral formulations. This project aims to advance quantum machine learning by: Designing a quantum counterpart of diffusion models; Leveraging path integral methods to model
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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