30 computer-security-"https:"-"https:"-"https:"-"https:"-"U.S"-"U.S" PhD positions at Cranfield University
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via joint activities with EU and UK cybersecurity hubs, preparing you for careers in trusted electronics, AI security, and national critical infrastructure protection. Graduates from this programme will
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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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system. This project will focus on improving the intake aerodynamic design systems in terms of design optimisation as well as computational modelling. Entry requirements Applicants should have a first or
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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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detection of chemical and microbial contaminants in rivers. The studentship is funded by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme, which is supporting new research
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health management (IVHM) system that leads to enhance safety, reliability, maintainability and readiness. Generally, prognostics models can be broadly categorised into experience-based models, data-driven
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Baseline Personnel Security Standard (BPSS) clearance. Funding Sponsored by EPSRC and Cranfield University, this studentship will provide a bursary of up to £25,805 (tax free) plus fees* for four years
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) and assess emerging governance opportunities and hurdles. England is facing growing challenges in long-term water security, with the Environment Agency predicting huge shortfalls in water supply in
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the world within a range of industrial and commercial sectors. These are Water, Agrifood, Energy and Power, Aerospace, Manufacturing, transport systems, Defence and Security and management.
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap