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simulation runtimes. During the PhD programme, a student would be expected to explore and analyse the limitations of possible solutions for parallel simulation of massively multicore computers. During
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Start date: 1st October 2026 The University of Nottingham is seeking an outstanding and highly motivated candidate for a fully funded PhD studentship focused on the development of next‑generation
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Description This PhD project aims to develop advanced software solutions for cryo-electron microscopy (cryo-EM) data analysis, modeling conformational heterogeneity, and identifying optimal binding candidates
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at understanding the interplay between the above physical effects on the microstructure evolution of Al alloys during additive manufacturing using the phase-field method. The PhD student will use an in-house phase
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temporal scales from seconds to hours and beyond. The aim of this PhD project is to build a multi-scale model linking molecular renewal to functional properties of synapses to study the relationship between
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extraction materials. Addressing these challenges requires fundamentally new approaches that integrate mineral liberation, molecular recognition, and process engineering. This PhD project contributes
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efficiency Optical neuromorphic computing is emerging as a promising alternative to classical electronic architectures, offering advantages in terms of speed, energy consumption, and parallelism. Nonlinear
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This PhD proposal is part of
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position within a Research Infrastructure? No Offer Description Work group: Institute of Materials Physics Area of research: PHD Thesis Part-Time Suitability: The position is suitable for part-time
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography Reference code: 2026