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discipline. Essential Good programming skills, preferably in Python/C#. Experience with machine learning, deep learning, or experimental AI evaluation. Interest in secure distributed AI, federated learning
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and corrective feedback. You will apply advanced algorithms for machine learning, multimodal biosignal processing, and human-state inference, working with shared-control strategies and electrotactile
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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applied mathematical modelling machine learning multi-fidelity modelling numerical methods. Demonstrated programming ability (MATLAB/Python/C++) and enthusiasm to learn PyTorch. Previous experience in one
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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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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Hey machine learning enthusiast with a love for physics and
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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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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