650 phd-sandwitch-in-architecture-and-built-environment PhD positions in United Kingdom
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Field
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skilled graduates through research projects that use industrial challenges as the catalyst for methodological advance. Work directly with leading industry partners Many of our PhD projects are developed and
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recently refurbished, state-of-the-art chemical biology laboratories at Newcastle University. The role may involve supporting the supervision and training of PhD students. Strong time management skills
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team and the School of Education, Communication and Language Sciences, including our Speech and Language Sciences subject area. The postholder should have a PhD in a relevant discipline e.g. Speech and
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, economic viability, and robustness to realistic operational uncertainty. PhD (or equivalent) in control engineering or closely related discipline. Track record in at least two areas: model predictive control
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Overview The International PhD in Applied Psychology is a partnership involving Nottingham Trent University, Ramon Llull (Barcelona) and Universidade Catolica Portuguesa (Porto) that provides a
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Experience Experience developing research software using appropriate languages and environements (Python, Julia, Matlab) Knowledge of optimisation problem formulations and solution methods Experience of risk
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piezoelectric composites and material architecture to achieve improved electromechanical coupling and spatial resolution would be important. In addition, they should have experience in sensor array readout
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considering a PhD or Clinical Doctorate, and many of our previous RAs have gone on to undertake these roles. The post holders will be based at Nottingham University and hold Honorary contracts with
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Framework level 12 (PhD) plus track record of emerging independence within a research/professional environment, or alternatively possess professional qualifications and experience equivalent to PhD level plus
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, at the University of Cambridge, UK. The Postdoc will work together with a team of students and research collaborators on the development of learning-based discovery of robot task/environment designs