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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
PhD candidate to develop and apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular
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Funded PhD studentship in AI-assisted materials discovery at the UK National Epitaxy Facility School of Electrical and Electronic Engineering PhD Research Project Competition Funded UK Students
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: University of Ulm, Germany Duration: 3 years Start date: August 2026 at latest The PhD position is based at the Institute of Biomedical Engineering at the University of Ulm, Germany. We Offer You: A World
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The PhD studentship will broadly explore development of cutting-edge AI solutions for image registration, anatomy segmentation, and immersive technology. The selected candidate will work with preoperative
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, Chemistry, Physics, Engineering, Mathematics, Computer Science, Data Science, Machine Learning or Artificial Intelligence a minimum 2:1 undergraduate degree (or equivalent) Excellent spoken and written
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for spinal surgery. The Candidates for this stipend should have a background in software engineering or similar and have substantial experience with machine learning. All cases involve various degrees of image
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speed - Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace
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: Prof. Walter Karlen Location: University of Ulm, Germany Duration: 3 years Start date: August 2026 at latest The PhD position is based at the Institute of Biomedical Engineering at the University of Ulm
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library. Strong interest in machine learning, reinforcement learning, and fluid dynamics. Ability to work independently and collaboratively in an interdisciplinary team. Excellent command of English, both
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Department/Location: Department of Biochemistry, Central Cambridge PhD Position - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based