44 postdoc-computational-physics-"Multiple" 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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scintillator-based radiation sensors combining multiple materials with complementary functions, offer a promising route to overcome these limits and achieve unprecedented timing resolution (sub-70ps), enabling
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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Multiple self-funded PhD positions are available in Modelling and Simulation (M&S). The project will aim to mature software repositories describing the biomechanics of the human brain. The M&S tools
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reusable launchers, autonomous robotics, and advanced materials could redefine how we design space structures. The ability to remotely assemble orbital systems from multiple launcher payloads would allow
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This is a self-funded opportunity relying on Computational Fluid Dynamics (CFD) and wind tunnel testing to further the design of porous airfoils with superior aerodynamic efficiency. Building
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-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
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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