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at the Division of Fluid Dynamics, within the Department of Mechanics and Maritime Sciences at Chalmers. The project is carried out in collaboration with Vattenfall Research and Development, and is part of
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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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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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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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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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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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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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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 1 hour 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