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geophysics or a closely related field such as applied mathematics or theoretical/computational physics. Master’s students may apply if they expect to complete their final examination by 01.10.2025. Employment
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this role? The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities for research, education and innovation. Candidates for these fellowships will be
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This PhD programme is multidisciplinary and cross-disciplinary building on applied mathematics and physics, technology and engineering—and the interplay between these. The programme has an applied
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applied physics or mathematics or a relevant engineering subject (e.g. mechanical, aerospace, chemical, or process safety). Master students can apply provided they complete their final master exam before
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promising research recruits the opportunity for professional development through studies towards a PhD-degree. The position is connected to the PhD program at the Faculty of Economics and Management and the
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. The employment period will be three years of full time with no teaching obligations, and there is a premise for employment that the PhD Research Fellow is enrolled in USN’s PhD program in technology within three
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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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: Applicants must hold a master's degree or equivalent education in applied physics or mathematics or a relevant engineering subject (e.g. mechanical, aerospace, chemical, or process safety). Master students can
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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