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teams to integrate AI and machine learning techniques into lattice field theory frameworks. - Engage in large-scale numerical simulations, performance analysis, and optimization using state-of-the-art
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Project title Multi-Modal Large Language Model for Medical Image Analysis Research period 2 years Abstract The proposed research project aims to develop a novel multi-modal large language model (MLLM
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Work type: Full-time School: School of Pharmaceutical Sciences / Innovative Drug Research Center Subject Area: Pharmacy, Medicinal Chemistry, Natural Product Chemistry, Pharmacology, Pharmaceutical
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Work type: Full-time School: School of Mathematics and Statistics Subject Area: Analysis, Algebra, Geometry, Equations, Operations Research, Biomathematics/Mathematical Biology, Image Processing
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Work type: Full-time School: School of Life Sciences Subject Area: Biochemistry and Molecular Biology (Mechanisms and Treatment of Major Diseases): Key protein functions, gene expression regulation
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departments, and wrote government policy reports; 3. Independently undertaking key project work in relevant professional fields and writing relevant reports or papers; 4. Participating in or leading related
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research scientist to work on the XENONnT Dark Matter experiment. About the Group: Led by Dr. Jingqiang Ye, our group is actively involved in both the hardware development and data analysis of the XENONnT