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variability to address specific research questions. Student profile: Essential skills: The student is expected to have a background in one or more of the following areas: environmental science/ mathematics
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should have a strong mathematical background, particularly in dynamical systems theory, and a keen interest in network science, and scientific computation. The student will gain invaluable experience
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background in Computer Science, Mathematics. Students with interests in machine learning, deep learning, AI, uncertainty quantification, probabilistic methods are encouraged to apply. For eligible students
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: Applicants must hold/achieve a minimum of a merit at master’s degree level (or international equivalent) in a science, mathematics or engineering discipline. Applicants without a master's qualification may be
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to exploit approximate or computational solutions; however, this can be unsatisfactory in that it lacks in valuable physical insight. This insight is often crucial in changing partial mathematical solutions
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Sciences, and Mathematics. Experimental experience in fluid dynamics and/or knowledge of any CFD codes would be an advantage, but not required as full training will be given. How to apply: Candidates should
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-level degree in Mathematics or Computer Science with an orientation towards Theoretical Computer Science. A strong background in logic and complexity theory is highly desirable and the candidate must
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-level degree in Mathematics or Computer Science with an orientation towards Theoretical Computer Science. A strong background in logic and complexity theory is highly desirable and the candidate must
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in a degree, ideally at Masters level, in an Engineering subject, Physics, Mathematics, Computer Science or other quantitative background. Knowledge in fluid mechanics, ocean waves, numerical methods
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degree, in a subject related to Computer Science, Mathematics, etc. You will be required to attend a zoom interview for acceptance. Interviews are planned for w/c 9 June 2025. How to Apply Applicants