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embrittlement. However, the immiscibility between Cu and W leads to poor bonding, whilst during high temperature manufacturing/operation embrittlement of steel-W joints occurs due to the formation of brittle
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platforms at both locations, providing the student with hands-on industrial experience as well as cutting-edge research insight. Description The global drive towards electrification in high-performance
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-Stokes equation are not eligible. Solving the BBGK equation is extremely challenging due to the high computational cost originated from its high-dimensionality. As a deterministic method, the potential
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-box techniques in the industry is still high. One of the main reasons is that the performance of such techniques highly depends on a large amount of good-quality data. Unfortunately, the availability
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that simulates real-world environmental conditions to test the durability and longevity of these materials and products made thereof, is also required. This PhD project aims to investigate novel high-performance
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Generative machine learning models have made significant progress in recent years. Typical examples include, for example, high-quality image or video generation using diffusion models (e.g
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in the USA. The main project for the student will be the computation of the properties of hadrons using lattice QCD and high performance computers. The PhD student will further investigate the use
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the name behind sustainable, high-performance ingredients and technologies in some of the world’s most successful brands: creating, making and selling speciality chemicals that are relied on by
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fuel, but its high reactivity makes it vulnerable to pre-ignition. The presence of lubricating oil droplets can worsen this risk by evaporating, altering chemical pathways, and producing nanoscale soot
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processing conditions (e.g. aging and thermo-mechanical processing route) to optimise corrosion performance of Constellium’s high strength alloys. This project is an exciting opportunity to work in close