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The Center for Machine Learning Research (CMLR) is a newly founded interdisciplinary research center at Peking University. Its goal is to advance machine learning-related research across a wide
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. The successful candidate will be involved in the gravitational wave astronomy research area as part of the GRAVITY research group, within the framework of the project "Ground-based Discovery Machines
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discovery. This position targets researchers whose primary methodological contribution lies in artificial intelligence, machine learning, probabilistic modeling, or their mathematical foundations, rather than
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-edge devices. The proposed research project will be at the edge between material science, physics, and engineering, combining high frequency dynamics with new computing paradigms such as Ising Machines
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develop intelligent equipment for plant phenomics. B. AI Engineers: 5 Positions Responsible for extracting and managing various types of corpus data and machine-generated data to ensure a stable data supply
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Position Description The project will focus on the development and application of advanced data-analysis techniques for gravitational-wave science, including machine learning and deep learning
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, engineering or physics. Knowledge: Computational programming, machine learning, quantum transprot, device simulation. Professional Experience: use of device simulation codes applied to 2D materials. Personal
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researchers returning to work in China. II. Priority Areas Artificial Intelligence Brain-Computer Interface and Brain-inspired Intelligence Embodied Intelligence Game Intelligence and Multi-Agent Systems AI
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contribute to project reporting. Work with the interdisciplinary team of the group (theory, computation, machine learning) and support junior researchers on SOT-related topics. Requirements: Education: PhD in
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Research Engineer - Tools developer for LSQUANT platform (Theoretical and Computational Nanoscience)
Personal Competences: Demonstrated competitive ability in using DFT simulations, and machine learning techniques and DFT. Demonstrated strong coding skills and a passion for UX/UI design. Summary