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, an extensive training programme in respect of industry-specific skills, and access to hotfire facilities at Westcott, Machrihanish, and elsewhere. You can learn more about the programme at r2t2.org.uk. Kick
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international collaborations with clinicians, regulators, policymakers, and industry partners. You must have a strong background in machine learning, computer vision, and medical image analysis, with publications
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machine learning for safe and optimal control of cyber-physical systems. The projects are expected to be funded by the VILLUM INVESTIGATOR project S4OS (“Scalable analysis and synthesis of safe, secure and
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
. CDDE provides access to advanced simulation tools and a collaborative environment focused on AI, digital twins, and immersive technologies. This project is co-sponsored by Rolls-Royce, offering unique
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PhD Studentship: Sleep and Circadian Rhythms in Elite Sport (Co-funded by Brighton & Hove Albion FC)
in machine learning methods for pattern recognition and prediction. An academic or practical background in sleep science and/or elite sport is highly desirable. The candidate will be embedded within
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these challenges by combining advanced metrology, data science, and machine learning to develop predictive models and new standards for recycled plastics. Based at the University of Manchester, in collaboration with
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deep learning. The purpose of this scholarship is to support a PhD student to contribute to the advancement of infrastructure monitoring technologies with strong industry collaboration. Student type
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meaningful questions, and possess the determination to pursue innovative solutions in a rapidly evolving technological landscape. DTU Wind and Energy Systems, in collaboration with Vestas - the world’s leading
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 4 hours ago
biology, (bio-)chemistry, and biophysics and have great interest in interdisciplinary research with an eagerness to learn and develop new approaches. Candidates should hold a Masters or equivalent degree in
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interest in environmental health and Exposome research; expertise in programming and quantitative data analysis, including machine learning in R/Python; affinity with bioinformatics; strong collaboration