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Field
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an appropriate subject (including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine learning techniques and experience with coding in Python is beneficial (but not a strong requirement
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methods for nuclear fusion, motivated by yield prediction in tritium fuel cycles. The lack of scalable tools necessitates large engineering tolerances, increasing reactor cost. Empirical tests are expensive
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the recording of substrate-borne vibrations produced by earthworms (and other invertebrates) in situ. To fully exploit this emerging technology there is a need to unravel the meaning within the data. As such
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equivalent) in Civil Engineering, Environmental Engineering, Hydrogeology, Geosciences, Environmental Sciences, or related STEM disciplines (e.g., Applied Mathematics, Physics, Computational Sciences
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A position exists, for a Research Assistant/Associate in the Department of Engineering, to work on experimental investigations of MILD combustion. The post holder will be located in Central
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potential. Our research focuses on Materials physics, Quantum technology, Soft & living matter, and Advanced energy solutions. Topics extend from fundamental research to important applications. We educate
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to applicants with a first-class or upper second-class degree (or equivalent) in any Engineering discipline, Physics or Maths. Please apply by submitting your CV, cover letter and transcript to
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. The project is co-sponsored by Spirent Communications, a world leader in navigation and testing technology. Spirent will provide advanced simulation tools, expert support, and industry placements to help make
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Department/Location: Department of Engineering, Central Cambridge We are seeking a highly motivated Research Assistant/Associate to join EPSRC and industry funded Digital Roads (DR) Prosperity
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/people/nc266.html and here https://www.eng.cam.ac.uk/news/engineering-better-car-experience . Four years funding is provided by the UK's Engineering and Physical Sciences Research Council (EPSRC) and JLR