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in image processing, quantitative analysis, and biological interpretation Proficiency in AI/machine learning tools for image segmentation, transformation, registration, or tracking Solid mathematical
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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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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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the public at large. Any other assigned duties Requirements: Candidates should be passionate about teaching and learning, hold a PhD or at least a Master's degree in related disciplines, and have
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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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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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market trends, incorporating factors such as weather patterns, consumption behavior, and regulatory changes. By leveraging advanced statistical and machine learning techniques, the role aims to provide
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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