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MR pulse sequences on Siemens platforms for quantitative imaging. Develop, adapt, and evaluate modern reconstruction algorithms for accelerated and robust qMRI. Collaborate with a multidisciplinary
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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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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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computational models, systems, and AI/ML tools using algorithms and analytics for materials and related physical sciences for a broad range of energy, transportation, and advanced manufacturing applications. In
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for the quantum era. The successful candidate will conduct interdisciplinary research on topics including: Security of quantum algorithms, quantum software, and quantum networks Quantum-safe cybersecurity
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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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large-scale genomic, phenotypic, environmental, and field trial datasets, you will benchmark AI and quantitative genetics methods, develop multimodal and hybrid modelling frameworks, and investigate
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research and implementation. You will also develop novel algorithms using state-of-the-art computer vision and machine learning techniques (segmentation, multimodal AI, foundation models, agentic frameworks
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experimental cycle from real-time X-ray measurements to post-experiment reconstruction: Develop learning-enabled algorithms for 3D reconstruction of noisy and heterogeneous synchrotron datasets. Implement