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push the limits of multiphysics CFD for laser manufacturing by developing a next-generation simulation capability for laser drilling (with relevance to additive manufacturing). Your work will capture
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within the climate change domain. The techniques are based on statistical and computational approaches, including machine learning algorithms. The project aims first to contribute to the prevention of fake
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development of wide bandgap device-based integrated motor drives, advanced control techniques for next-generation drives, and innovative approaches to reducing passive components in electric drive systems. Your
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maintaining human oversight for quality and accuracy. This real-world case provides a unique opportunity to study how trust in AI systems develops, whether specialists seek to preserve authority in decision
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predict the hair line. Design a classification algorithm to quantify the hair loss severity. Develop a mobile phone app as point of care for people with alopecia. Evaluate novel AI assessments using
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” because early abnormalities are missed by current diagnostic methods. This project sits at the cutting edge of biomedical engineering, AI, and respiratory medicine, developing a non-invasive, low-cost
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propagation models that incorporate the effects of fire effluents, validated through controlled experimentation. You will develop tomographic inversion methods and anomaly-detection algorithms capable
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interpreted by regression and tree-based machine learning algorithms to obtain even better mutants and develop mechanistic hypotheses. Various collaborations with ON-TRACT network partners across Europe allow a
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prestigious £8M EPSRC Programme Grant on Advanced Integrated Motor Drives (AIMD), a major research initiative launched in 2025. You will work on the development of wide bandgap device-based integrated motor
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between things, developing the mathematical and computational foundations for optimal information transfer between surfaces and volumetric current distributions in complex scattering media. The research