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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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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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Analysis, Pharmaceutics, Bioinformatics, Total Synthesis of Natural Products, Microbiology and Biotechnology, Computer-Aided Drug Design, Chemical Biology Introduce: The School of Pharmaceutical Sciences
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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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strong policy analysis ability and solid scientific research ability, and those with high-quality publication records are preferred; 4. There is no age limit for applicants, and those who have participated
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; microbial resource utilization; development of entomopathogenic fungi as biopesticides. Genetics (Genetics and Bioinformatics): Evolutionary genomics; molecular analysis of insect domestication traits
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video analysis). Applicants must have, or be close to completion of a PhD in mathematics, physics, statistics, biomedical engineering, computer science, or another related field. Applicants should have
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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
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conjecture, -- Langlands program and related problems, -- algebraic geometry and complex geometry, -- partial differential equations and in particular Navier-Stokes equations, -- stochastic analysis