18 computer-"https:"-"https:"-"https:" "https:" PhD positions at The University of Manchester
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will develop nanoscale devices which use magnons (collective excitations in magnetic order) as information carriers for ultra-efficient, compact, brain-inspired computing. This project is positioned
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by advances in additive manufacturing and computational design. Architected lattice metamaterials—structures whose properties arise from geometry rather than composition—offer unprecedented
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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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comprehensive programme of training to support both technical and professional development. Core technical training will include in-house numerical modelling sessions, supplemented by access to specialised FEFLOW
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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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decarbonization of existing maintenance and decommissioning of assets. Programme structure (1+3)* Year 1 (Taught component): All students spend the first year at The University of Manchester undertaking taught MSc
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(wind, solar, geothermal, tidal, hydrogen) and nuclear (fission and fusion), and to support the decarbonization of existing maintenance and decommissioning of assets. Programme structure (1+3)* Year 1
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also researched by the recently commenced multi-million MOSFET (The Maturing Optimised Solutions for Aerospace Technology) research programme lead by RR in collaboration with UoM and an electrical
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. Feedstock choice, regional dynamics, and process side-streams all affect costs, energy use, and emissions. This PhD project will develop advanced computational models to address these combinatorial decision
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computational fluid dynamics (CFD) simulations of blood flow through arteries and develop a cutting-edge super-resolution framework using convolutional neural networks (CNNs). The ultimate goal is to vastly