44 postdoc-computational-physics PhD positions at Cranfield University in United Kingdom
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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equivalent in a relevant discipline such as aerospace engineering, mechanical engineering, electrical engineering, computer science/engineering, applied physics/mathematics, or related fields. Prior experience
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and advanced material design and fabrication. Through this multidisciplinary project, the student will develop expertise in: Hands-on experience with advanced computational physics and materials
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dosing in slowsand filters. This project suits graduates seeking careers in drinkingwater technology, sustainable infrastructure, and lowcarbon process design. Drinking-water production is under mounting
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with: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in
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accuracy is still limited. In contrast, computational fluid dynamics (CFD) models can capture the arc physics and molten pool dynamics, including arc energy transfer and liquid metal convection within
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critical to ensuring the longevity and safety of fusion reactors. This PhD project focuses on developing an integrated framework that combines cutting-edge computational models, including Monte Carlo
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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modelling tools to understand and tailor the physical and chemical interactions at the interfaces within metascintillators. Cranfield University’s Centre for Materials is internationally recognised
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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing