22 quantum-computing-"https:"-"https:"-"https:"-"Univ" PhD positions at University of Warwick
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developer and user meetings. You will acquire skills in programming (e.g. Python, FORTRAN, bash) development of quantum chemistry software and stand-alone tools a wide range of computational and quantum
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that is still poorly understood. This project will develop advanced computational models to simulate a new imaging technique called electron ptychography, which can map magnetic fields in 3D at nanometre
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, including in liquids. Combining quantum mechanics and atomic simulation with AI-driven sampling techniques, you will determine terahertz and Raman spectrograms to directly compare to measurements obtained in
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Electronics, will use computational simulations to study how thin films form during flowable chemical vapor deposition (FCVD), a process used to build advanced semiconductor devices. Unlike traditional CVD
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rigorous theoretical framework to interpret the measurements is still lacking. This project addresses this gap by combining quantum mechanical calculations with continuum micromagnetic theory to bridge
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A 4-year PhD position is available as part of the EUTOPIA PhD co-tutelle programme, working jointly in the groups of Mark Greenhalgh at the University of Warwick and Frank De Proft and Mercedes
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(UK or worldwide) Research Summary: We are advancing a range of projects focused on chalcogen bonding across synthetic, catalytic, analytical, and computational chemistry. Chalcogen bonding is an
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Centres Research area and project description: Artificial intelligence (AI)-based technologies are being adopted across various sectors at an unprecedented scale. However, the computing resources required
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. The candidate should have a good 2.1 Bachelors, or Masters degree in Electronic Engineering, Computer Sciences or equivalent. Experience in communications and networking, AI, or robotics is desirable but not
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About the project: Supervisor: Professor Nicholas Hine, University of Warwick This project uses cutting-edge computational and machine learning methods to accelerate catalyst discovery for fuel cell