76 information-security "https:" "https:" "https:" "https:" "https:" "Dr" "Robert Gordon University" PhD positions at University of Nottingham
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This exciting opportunity is based within the Building, Energy and Environment Research Group at Faculty of Engineering, which conducts cutting edge research into the sustainability of buildings and
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/Medicine/Rehab, Public Health, Medicine). Informal enquiries may be addressed to Dr Thomas Bestwick-Stevenson, thomss.bestwiick-stevenson@nottingham.ac.uk To apply, candidates should send their CV and a
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) Yan (rundong.yan@nottingham.ac.uk ), Dr Alistair Speidel (Alistair.Speidel@nottingham.ac.uk ), or Dr Rasa Remenyte-Prescott (r.remenyte-prescott@nottingham.ac.uk ) This studentship is open until filled
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to Sport Science/Medicine/Rehab, Public Health, Medicine). Informal enquiries may be addressed to Dr Thomas Bestwick-Stevenson, thomss.bestwiick-stevenson@nottingham.ac.uk To apply, candidates should send
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A Human-Factors Investigation of Automation, Decision-Support and Machine Learning in Clinical Decision-Making Tasks. This PhD project is based within the Human Factors Research Group in the Faculty
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year for consumables and travel. Funding from MTC requires passing their security checks before starting the PhD. Vision We are seeking a PhD student that is interested in robotics and automation
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Location: Faculty of Engineering, University of Nottingham, UK Start date: October 2026 Funding: EPSRC Doctoral Landscape Award Duration: 3.5 years This exciting opportunity is based within the Thin
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MEng degree in Electrical and Electronics Engineering or Aerospace Engineering. To apply or for further information, please contact Dr Sharmila Sumsurooah Sharmila.Sumsurooah@nottingham.ac.uk Funding
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Towards a Flower Waste Biorefinery: Predictive Enzyme Design for Bio-Based Chemicals This exciting opportunity is based within the Faculty of Engineering, which conducts cutting-edge research in
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The rapid growth of deep learning has come at an extraordinary environmental and computational cost, yet the standard training paradigm remains remarkably unchanged. Every sample is passed through