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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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on date of birth and date of receiving the PhD degree, research statement, publication list, and at least three reference letters. The deadline is January 10, 2026 and applications received before this date
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using state of the art HTS equipment. Qualifications 1. PhD in any biological or medical science. 2. Desirable but not required, membrane protein purification, structure determination, molecular biology
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Detection and Neutrinoless Double Beta Decay (PandaX) 2. Cosmic Neutrino Experiment (TRIDENT, JUNO, IceCube) 3. Reactor Neutrino Experiment (JUNO) Faculty members involved are Profs. Donglian Xu, Jianglai Liu
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. Fresh PhD graduates are encouraged to apply. The candidates are expected to: Carry out novel research and publish results at top conferences/journals. Contribute to supervision and training of graduate
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the level of post-doctoral researcher with two-year term. The Position will remain open until filled. Fresh PhD graduates are encouraged to apply. The candidate is expected to: Carry out novel research and
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of research projects, complete experimental tasks, and write research papers. Responsible for writing and participating in subject experiments. Qualifications 1. PhD in relevant biomedical or biochemical
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for various omics data, including microbiome, genetics, genomics, and metabolomics data. The position is for a two-year appointment. Applicants must hold a PhD in Statistics, Biostatistics, Bioinformatics
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provided directly from the referees). In addition, the candidate should identify a potential mentor who is a permanent member of the IASM in the application letter (for a potential mentor, please find in
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: • Precision measurement of oscillation parameters and mass ordering through the analysis of reactor neutrinos • First detection of diffuse supernova neutrino background • Supernova burst neutrinos in the multi