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for their employability in applications. Additionally, machine learning methods need to be applicable to high-dimensional and to noisy data that are typically encountered in real-world applications. The aim of this project
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remaining functional under harsh geothermal conditions. The candidate is expected to work closely with D-TECH and may undertake a secondment period to test and apply the developed methods on industrial-scale
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associated numerical methods and AI, will be used with High Performance Computing (HPC) to improve understanding of key flow physics and inform future HPT design. Skills and Experience Required: Applicants
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profound interest in inorganic chemistry, both in experimental and modelling applications. We are looking for candidates who are also interested in the analytical and numerical aspects of the work
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, and a solid understanding of numerical analysis and familiarity with the use of analytical tools. They should also have knowledge and experience in parallel coding and spectral methods. They must have
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methods. Depending on the strengths and interests of the PhD candidates, the PhD project will focus on some of the following aspects: The quantification of the fundamental physical and kinetic differences
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Primary Supervisor -Prof Michal Mackiewicz Scientific background Marine litter is a key threat to the oceans health and the livelihoods. Hence, new scalable automated methods to collect and analyse
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spectroscopic methods suitable for large-scale sample screening and eventual field deployment. The project will also involve developing your skills in data science, including multivariate analysis, machine
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physics all the way to numerical simulation algorithms? Then apply now to join our team of researchers in the Quantum Information and Quantum Many-Body Physics research group. Your personal sphere
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fuels (hydrogen, methanol, ammonia), simulation tools for marine engines and/or fires due to fuel leakages, data analysis methods and their applications for ships, sufficient understanding of appropriate