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applicant must have (or be close to obtaining) a relevant PhD in Fluid Mechanics from an Engineering, Mathematics or Physics Department, a strong background in theoretical and computational fluid mechanics
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of Engineering at the University of Birmingham and delivered in collaboration with industrial partners and the Quantum Technology Hub in Sensing, Imaging and Timing (QuSIT) – one of five national hubs funded by
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to apply: Please choose Electrical and Electronic Engineering Research Program and Control and Power Group, then indicate Professor Balarko Chaudhuri as a potential supervisor when making the application
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) offer new avenues to tackle this problem. AI models have demonstrated strong potential in clinically relevant insights from electrical signals such as ECGs, and from cardiac imaging modalities including
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for Security Operations Centres (SOCs) while pioneering strategies for quantum-era resilience. This project sits at the intersection of Artificial Intelligence, Cybersecurity, and Explainable Computing. It
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) offer new avenues to tackle this problem. AI models have demonstrated strong potential in clinically relevant insights from electrical signals such as ECGs, and from cardiac imaging modalities including
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The successful candidates should preferably have a master’s degree in chemistry, or chemical-, process-, energy- engineering or related fields. Fluent written and verbal communication skills in English are
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: Advanced molecular and protein analysis Mass spectrometry-based imaging Multi-omics technologies Preclinical cardiometabolic animal models They will also gain professional development in data stewardship
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are part of the programme. The research is funded by the Centre of Propulsion and Thermal Engineering at Cranfield University. The work will be conducted at the Cranfield icing wind tunnel (IWT) based
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with mechanical testing of the alloys in hydrogen rich environments will be used. The ReNU+ CDT has hosted a number of online pre-application workshops to provide guidance on the application process