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Field
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machine learning. It focuses on developing innovative algorithms and models to address complex problems in diverse fields such as robotics, healthcare, and finance. The department offers a range of
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of machine learning theories and algorithms; inter-disciplinary background; A combination of education and relevant experience from which comparable knowledge is demonstrated may be considered
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, and construction-task constraints, supporting real-time 3D visualization, human monitoring, algorithm testing, and pre-assembly validation. The platform will serve both as a research environment for
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supports research in fundamental AI theory, core algorithms, scientific computing, and AI-enabled applications. The Institute is located at the Binghai Bay Campus of Great Bay University, in a provincial
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intelligence. This part focuses on robotic agents operating in unstructured 3D environments (homes, offices, industrial sites, shopping malls). The work centres on (a) model-based and hybrid RL algorithms built
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the supervision of Dr. Jun Xu Develop high-quality models, algorithms, experiments, or integrated modeling-characterization frameworks Publish first-author papers in leading peer-reviewed journals Contribute
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algorithm design skills Expertise on ML tools for chemistry, in particular, generative AI Experience with Python, ML, and AI for chemical applications Job Description: A Post-doctoral Associate in Theoretical
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environment. Primary Responsibilities Lead the state-of-the-art research using genetic mouse models for evaluating therapeutic efficacy, histology, and advanced imaging assays for glioblastoma research. Utilize
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sensitivity analyses across pathology severity, body morphology, camera viewpoint, and environmental conditions Develop a single-camera, home-based markerless system Test the algorithm across lab and home
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-camera, home-based markerless system Test the algorithm across lab and home-recorded environments to assess real-world performance Design clinician-facing visualization tools ensuring privacy-by-design