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systems and control theory, circuit theory, optimization, and machine learning, with the ultimate goal of advancing the mathematical foundations of physics-based learning. Your responsibilities include
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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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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
We welcome applications from candidates with a broad range of academic backgrounds and experiences for a 4-year PhD project on Mathematical and Machine Learning Aspects in Crystallography at Durham
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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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PhD studentship: Discovery of rational therapeutic biomarkers in breast cancer by systems pathology and deep learning Supervisor: Dr Hamid Raza Ali Department/location:Cancer Research UK Cambridge
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staff position within a Research Infrastructure? No Offer Description How can institutions learn to govern technologies whose capabilities, risks and societal effects are still emerging? REGULAIRE offers
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engineering disciplines, including structural mechanics, hydrodynamics and machine learning Strong programming skills in Python and/or MATLAB Experience with scientific computing, CFD/FEM software, potential
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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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. 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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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