42 parallel-and-distributed-computing-"Multiple" PhD positions at Cranfield University in United Kingdom
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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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scintillator-based radiation sensors combining multiple materials with complementary functions, offer a promising route to overcome these limits and achieve unprecedented timing resolution (sub-70ps), enabling
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We are seeking a highly motivated candidate to undertake a PhD program titled "3D Temperature Field Reconstruction from Local Temperature Monitoring in Directed Energy Deposition." This exciting
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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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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. The studentship is funded by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme, which is supporting new research on human-environment interactions in freshwater ecosystems. There is an
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covers fees and stipend for a home (UK) student with funding provided by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme. Options exist for PhD and Master + PhD routes
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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research and strong written and oral communication skills, as we can complete existing strengths with targeted training. Funding This fully funded Connected Waters Leverhulme Doctoral programme studentship
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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