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enable more targeted mitigation measures. Your investigation of this research question will be principally numerical, employing computational fluid dynamics to produce high resolution simulations which
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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep
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Overview You will be a member of the Particle Physics and Particle Astrophysics (PPPA) group in the School of Mathematical and Physical Sciences. We are seeking to appoint three fixed term Research
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research. The project will involve computational modelling using physics- and machine learning-based methods and would suit a top student with a background in Physics, Engineering, Mathematics or similar
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, mathematics or related field. Experience with computer programming languages (e.g., MatLab, Phyton), familiarity with geographic information systems and systems thinking methodologies and an interest in
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or second-class honours degree in engineering, physics, mathematics, computer vision, or a related field. Interest or experience in computational modelling or coding—beneficial but not required. A
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. Successful candidates should have a background in applied mathematics/theoretical physics/computer science or related discipline, alongside strong computational skills. Value: Home fees paid and stipend
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Experimental Particle Physics to join the Sussex group working on the SNO+ experiment under the supervision of Dr Elisabeth Falk. SNO+ offers a rich programme of neutrino physics, which includes neutrinoless
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and multidisciplinary approach to such questions, bringing together knowledge in fields as diverse as, e.g., mathematical logic, the theory of computation, software engineering, artificial intelligence
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an interest in data science, computational chemistry or related areas. A degree in Chemical Engineering, Chemistry, Computer Science, Mathematics or other engineering/science disciplines with significant