28 software-engineering-model-driven-engineering-phd-position Postdoctoral positions at University of London
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You will have a PhD in Computer Science or a related discipline or will have obtained it by commencement of the position. Successful candidates will have experience of model training methodologies
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have a PhD and track record in either computer science with specialisation in relevant AI technologies for surrogate modelling, or in Earth or Environmental Science with a strong track record in
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clinical workflows and validate the established systems in tandem with clinical partners. About You You will possess a PhD (or be nearing completion) in mechanical, electrical, or biomedical engineering, or
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dynamic strain and flow fields during flight. Candidates should hold a PhD in a relevant biology or engineering discipline and be competent with numerical simulations. Desirable competencies would include
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About the Role This Postdoctoral Research Associate (PDRA) position is part of an exciting EPSRC-funded programme, "Enabling Net Zero and the AI Revolution with Ultra-Low Energy 2D Materials and
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model development, and/or ethical policies and issues of live performance, and in particular how emerging trends around technology mediate performance. The University has adopted hybrid working for some
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and Immigration website . Full-Time, Fixed-Term (36 months) We are looking for a highly motivated early career researcher with a PhD (or near completion) in psychology, life sciences, genetics
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infrastructure enables recruitment of 200-300 severely injured patients annually as part of the ACIT study. We also have a well-established experimental modelling group with full ethical approvals in place for all
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help supervise BSc, MSci, and PhD students. The successful applicant will have a PhD in Astrophysics, Theoretical Physics, or a related discipline and prior experience relevant to the post. It is also
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal