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Your profile PhD applicants must possess a Master's degree in mathematics, theoretical physics, or computer science. Candidates should have an exceptional academic record and a robust mathematical foundation. Candidates are also expected to have strong coding and implementation skills, with the...
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applications, including solving mathematical reasoning problems and tackling the Abstraction and Reasoning Corpus (ARC) challenge among others. The ideal candidate has a strong background in machine learning and
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overuse injuries. Wearable sensors to quantify of the impact and benefit of sleep on the recovery, performance and overall wellbeing of athletes. Using big data and machine learning methods to identify
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, into the research groups of Prof. Oliver Buxton whose expertise is on turbulence, wind-energy flows, and turbulent cloud microphysics and Prof. Luca Magri whose expertise is in scientific machine learning
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processing, embedded systems, machine learning, and networked communication. Each PhD position corresponds to a dedicated research topic within the consortium. All doctoral researchers will benefit from joint
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. What you should have: A 1st degree in physics or engineering. An interest in optics, some ability in computer programming A desire to learn new skills in complementary disciplines. You will work jointly
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, Ultrasound and Vibration, Aircraft Structures, Damage Assessment, Structural Health Monitoring, Structural Health Prognosis, Bayesian Statistics, Machine Learning Informal enquiries prior to making
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develop methodologies (such as acoustic emission method) detecting early signs of damage, leaks, or degradation before they become critical. We will also leverage the latest developments in machine learning
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certification, dramatically accelerating innovation cycles. What you will gain: Expertise in Finite Element Analysis, Scientific Machine Learning, Uncertainty Quantification, and Professional Programming
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programming languages such as Python and experience with deep learning frameworks (e.g., PyTorch, TensorFlow) is highly desirable strong interest in interdisciplinary research combining imaging, machine