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PhD Scholarship Opportunity - Processing intelligence for green metals using in situ X-ray characterisation and machine learning Job No.: 693787 Location: Clayton campus Employment Type: Full-time
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. Our community is open and informal, with more than 7,000 students, 1,000 PhD students, and 1,400 staff members from all over the world. If you would like to learn more about the Faculty of Science and
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of hybrid intelligence—humans and machines working and learning together. Our mission is to establish an internationally leading interdisciplinary hub that advances foundational research, responsible
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under global change requires accurate and consistent soil information at global scale. Current global soil maps are derived using empirical machine learning that often ignores known soil processes
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31.07.2026, Academic staff We are seeking a researcher in Scientific Machine Learning (SciML) to join the project "Data science at scale" at the Technical University of Munich, Germany. Ideal
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PhD Studentship: Designing Human-AI Teams for Meaningful Human Control of 'Machine-Speed' Operations
January 2027. This PhD explores the design of human-AI teams when the AI agents operate at ‘machine-speed’ (i.e., significantly faster than human cognitive and communication capabilities). The research will
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) and/or machine learning (about 10 PIs). The Physics Laboratory is about 180-member strong and conducts world-leading research on a broad range of topics, including quantum technology, statistical
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paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported Collaborative Learning (CSCL), Computer-Supported
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Brushless Doubly Fed Machines (BDFMs) are emerging as a promising technology for offshore wind turbine generators due to their fractional-sized power converters, elimination of rotor brushes and
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Learning Matters!. Task You will break with the current focus on the brain to uncover the physics of continual learning instead by investigating the emergence of learning bottom-up in life, reduced in