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motivated PhD student to join our interdisciplinary team to help address critical challenges in high-speed electrical machine design for electrified transportation and power generation. Together, we will make
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an international reputation in AI and Machine Learning, Cyber Security, 5G and Next Generation Communication Systems and Advanced Materials Innovation. The teaching is organised at a School level and you will join
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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a role model and fostering an inclusive working culture. Person Specification PhD, or close to completion, in a relevant, quantitative field, e.g. meteorology, machine learning, climate science
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together with relevant experience. You will have a strong technical background in machine learning, especially RL and LLMs. An ability to work independently and as part of a collaborative research team is
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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or machine learning to complex data. The successful candidate will have (or be nearing completion of) a PhD in a relevant field such as polymer science, materials engineering, or mechanical engineering
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Introduction to Programming with Python Supervised Machine Learning Expected Qualifications: Minimum of a Master’s degree related to the area of teaching, with a preference for an earned doctorate (PhD or DBA
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member of either our Electrical Power or Fluid Mechanics research nodes. The Electrical Power Research Node comprises a group of eight academics with seven research associates and 20 PhD students. We have
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our students About you For a Lecturer post you will: Possess sufficient breadth or depth of specialist and core knowledge in the discipline, demonstrated by a PhD or equivalent in the field