40 phd-computational-"FEMTO-ST"-"FEMTO-ST" PhD positions at Cranfield University in United Kingdom
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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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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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This self-funded PhD research project aims to develop smart sensors based on low-frequency resonance accelerometers for condition monitoring of ultra-speed bearings. The developed smart sensors will
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
Fully funded PhD at Cranfield University, supported by the EPSRC DTP and Rolls-Royce. This 3-year project covers tuition fees, a tax-free stipend, and funding for training, conferences, and a
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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 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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We are pleased to announce PhD studentship project in “Advanced Composites Development for Hyper-velocity Impact Protection of Space Satellites Structures”. This is an exciting PhD research
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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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This PhD project will focus on developing, evaluating, and demonstrating a framework of novel hybrid prognostics solution for selected system use case (e.g. clogging filter, linear actuator, lithium
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. The integration of AI into hardware not only enhances performance but also reduces energy consumption, addressing the growing demand for sustainable and efficient computing solutions. This PhD project delves