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system model new module integration, scenario simulations, and prognostics analyses Physics-informed deep learning/hybrid modeling/reasoning AI algorithm development and optimization Job Requirements: A
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and support all aspects of the research project. Carry out algorithm development and data analysis, including (but not limited to) single-cell and bulk transcriptomics, spatial omics, proteomics, and
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framework to find the optimal operation strategy Conduct computer programming to verify the efficiency of the designed solution algorithms Analyze data acquired from the field survey Develop machine learning
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algorithms for: Fine-grained video understanding Concept learning Temporal reasoning Vision-Language Models (VLMs) Multimodal representation learning Self-supervised and weakly supervised learning Fine-tune
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to advance precision medicine. Dr Chen’s research team develops AI algorithms for spatial and single-cell omics, integrates publicly available datasets into a centralised data hub, and leverages this platform
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. • Develop novel algorithms and methodologies for defect detection, classification, and failure analysis. • Supervise and mentor junior researchers and graduate students in related research activities. 2
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grow. We welcome you to join our community of faculty, students and alumni who are shaping the future of AI, Data Science and Computing. Key Responsibilities: Designing and developing scalable algorithms
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Management Science journals, and/or the MSOM journal. The candidate should be able to understand the model, the algorithm(s), the main results and the proofs in a paper. • Experiences with submitting (new
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on Multimodal Intelligence. The role involves developing and advancing novel algorithms for emerging challenges in Multimodal Intelligence, including Multimodal understanding and generation. The candidate is also
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, converter control, and energy systems. Design, model, simulate, and develop advanced power electronic converter topologies and associated control algorithms. Investigate high-efficiency and high-power-density