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This PhD opportunity at Cranfield University invites candidates to explore the integration of AI into certification and lifecycle monitoring processes for safety-critical systems. The project delves
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This exciting fully funded PhD, with an enhanced stipend of £25,726 per annum (with tuition fees covered), is sponsored by Anglian Water and EPSRC. It directly tackles one of the central challenges
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This PhD opportunity at Cranfield University invites candidates to pioneer research in embedding AI into electronic hardware to enhance security and trustworthiness in safety-critical systems
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This exciting fully funded PhD, with an enhanced stipend of £25,726 pa, is sponsored by Anglian Water, Thames Water, Yorkshire Water, Northumbrian Water and EPSRC. The research will address
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. At a glance Application deadline01 Apr 2026 Award type(s)PhD Start date01 Jun 2026 Duration of award3 years EligibilityUK, EU, Rest of world Reference numberCRAN-0039 Entry requirements Applicants
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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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Cranfield is an exclusively postgraduate university that is a global leader for transformational research and education in technology and management. Research Excellence Framework 2014 (REF) has recognised 81% of Cranfield’s research as world leading or internationally excellent in its quality....
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We are looking for a highly motivated candidate to pursue a PhD programme titled "CFD-informed finite element analysis for thermal control in wire-arc directed energy deposition." This research
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. At a glance Application deadline01 Apr 2026 Award type(s)PhD Start date01 Jun 2026 EligibilityUK, EU, Rest of world Reference numberSATM606 Entry requirements Applicants should have an equivalent
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
We are pleased to announce a self-funded PhD opportunity for Quantitative assessment of damage in composite materials due to high velocity impacts using AI techniques. Composite materials, such as