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Field
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PhD Research Fellow in Experimental Fluid Mechanics: Tunable hairy surfaces for droplet flow control
of the fellowship is research training leading to the successful completion of a PhD degree. The fellowship requires admission to the PhD programme at the Faculty of Mathematics and Natural Sciences. The application
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, methods and applications. The areas represented include: fluid mechanics, biomechanics, statistics and data science, computational mathematics, combinatorics, partial differential equations, stochastics and
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Discovery, Development Sciences, and Aesthetics which includes fields such as chemistry, biology, pharmaceutical science, and computational information sciences. This enriching training program offers a
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the results of this project. Candidates must possess a good first Degree (or Master's) and PhD (or near competition) in Engineering, Mathematics, Physics, Computer Science, or related disciplines. Your working
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Cryptography o Data Sciences, Complex Networks, Mathematical Biology o Quantum Computation & Information Science o Post-Quantum Cryptography, Homomorphic Encryption and Computing o Theoretical and Computational
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combining the mathematical and computational cultures, and the methodologies of statistics, logic and machine learning in unique ways, Integreat's machine learning will solve fundamental problems in science
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quantitative field like economics, mathematics, computer science, or engineering, or related area of study [or 5+ years of relevant coursework or professional research/engineering experience]; 5+ years
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: □ Computational Sciences o Cryptography o Data Sciences, Complex Networks, Mathematical Biology o Quantum Computation & Information Science o Post-Quantum Cryptography, Homomorphic Encryption and Computing o
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Sciences, Computing Science and Mathematics, Psychology and the Institute of Aquaculture. FNS is a distinctive academic arena where new fundamental understandings of the complex and challenging inter
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computer science or statistics A solid background in mathematics, linear algebra and statistics. Documented experience with Bayesian spatiotemporal modelling, including experience with the INLA framework