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position in statistics, machine learning, and data science. The postdoctoral researcher will be mentored by Professor Yiyuan She and will contribute to the development of innovative statistical methods and
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/ Theoretical Particle Physics Lattice QCD and Heavy Ion Physics Machine Learning / Machine Learning Lattice Field Theory lattice gauge theory (more...) Nuclear Physics / Lattice QCD Appl Deadline: 2025/09/30
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processing 2. nonequilibrium physics of molecular machines 3. understanding the role of phase-separated condensates with quantitative models 4. Modeling multicellular systems in mechanobiology 5. AI assisted
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researchers in pursuit of advancing knowledge and making significant contributions to their respective fields. ESSENTIAL QUALIFICATIONS/EXPERIENCES PhD Graduation; Strong background in deep learning, machine
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, separation, and catalysis, with a focus on carbon capture and conversion technologies. Artificial Intelligence: Leveraging AI and machine learning to optimize material design and catalysis processes. Carbon
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particle physics or related areas prior to the time of employment. Preferences will be given to those with experiences in collider phenomenology, machine learning, effective field theories, positivity bounds
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Recruitment Conditions: 1. Hold a doctorate degree in condensed matter physics, materials physics, or a related field. 2. Have expertise in at least one of the following areas: growth of van der
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) Excellent previous research record. 3) Proven ability to learn relatively fast advanced areas of modern number theory and/or geometry. 4) Good record of delivered talks and participation in seminars and
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. - Research Areas: Positions are focused around Deep Learning and Inverse Problem Regularization. Successful candidates will engage in diverse projects ranging from provincial to national levels, in
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of expertise of an applicant are pure, applied or computational mathematics. The successful candidate will have no obligation to teach, but will be required to apply for an external funding from National Natural