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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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PhD degree in theoretical physics or related subjects. Eligibility: Candidates of all nationalities are welcome to apply. The working language is English. Duration: 2 year with possibility of extension
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or continuously monitord systems are especially encouraged to apply. However, other research profiles, within the realm of theoretical physics, especially will also be seriously considered. Requirements: A PhD
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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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candidates will work in the neutrino group of the experimental physics division (EPD) at IHEP on one of the following topics: • First Detection of Diffuse Supernova Neutrino Background • Supernova Burst
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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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opportunities, propose new ideas, and seek solutions to research challenges; Collaboration: The ability to communicate effectively, listen actively, and contribute constructively to group discussions is essential
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airports, convenient stores, hospitals etc. Job Requirements: We are seeking driven and collaborative applicants with the following qualifications: 1. a PhD in mathematics or a related discipline, preferably
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around the world, and completing the special construction work of the Institute and other work related to the Institute. Qualifications and Requirements: 1.A PhD degree in a relevant discipline, preferably
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, depending on qualifications. Most postdocs at BIMSA receive generous travel grants. BIMSA also offers generous benefits including subsidized housing and health insurance. PhD in one of the fields represented