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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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Particle Physics. An official proof of Ph.D. graduation by the time the position starts is strongly preferred. • Strong background in particle physics and familiarity with programming languages commonly used
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with programming languages commonly used in particle physics research. • Demonstrated ability to conduct independent research and a record of publications in peer-reviewed journals. The successful
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collisions at the LHC (ATLAS) and prospective future colliders (CEPC/SppC); and related theoretical efforts. The division has an active visitor program and co-organizes several conferences and workshops
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written and verbal communication skills in English. 4. Preference will be given to candidates experienced in using Sage, PARI/GP or Magma to do number theoretic computations (L-functions, modular forms, etc
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, provincial and municipal talent programs. Requirements: Ph.D. in Mathematics, Cryptography, Computer Science, or closely related areas. Proven track record of research in post-quantum cryptography, or a
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, education allowance, relocation support etc. Adequate research funds, and university’s supports to apply for national, provincial and municipal talent programs. Requirements: Ph.D. in Mathematics
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of Intelligent Computing and MoE Key Lab. Additional funding available for recipients of prestigious fellowships: Postdoctoral Innovative Talents Support Program Postdoctoral International Exchange Program
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. 3. Fluent in English. Application Materials Required: Further Info: https://www.cimrbj.ac.cn/en/channel/1864142817505447936.html 10 Xitoutiao, Youanmen Wai, Fengtai District, Beijing 100069, China
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countries, including EPSRC Programme grant "Symmetries and Correspondence". The principal investigator is Professor Ivan Fesenko. Professor Ivan Fesenko's Higher Number Theory page: https://ivanfesenko.org