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University of Stavanger Fellowship ID: 3524 -PHD1 [#26679, 30239055] Fellowship Title: PhD Fellowship in Algebraic and Symplectic Geometry Fellowship Type: Student programs Fellowship Location
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candidate with a strong background in computational fluid dynamics (CFD) and specialized expertise in hemodynamics associated with coronary artery disease (CAD). The ideal candidate will hold a PhD in
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Location: Belval, Luxembourg 4364, Luxembourg [map ] Subject Area: Geometry and Algebraic Geometry Appl Deadline: 2025/08/31 11:59PM (posted 2025/05/13) Position Description: Apply Position Description
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Vacancies PhD position on Stochastic geometric numerical methods Key takeaways Are you passionate about developing cutting-edge numerical algorithms at the intersection of geometry, stochastic
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mathematics (Pure Mathematics: Analysis, Algebra, Geometry and Topology, Algebraic Geometry, etc. Applied Mathematics: Information Theory, Coding and Cryptography, Mathematical Foundations of Signal Processing
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research activities to complement the current expertise of the present SISSA faculty members, as well as to attract external funds, take an active part in the SISSA PhD teaching programme, and supervise
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
of Munich (TUM), Campus Heilbronn. We are looking for exceptional candidates who are interested in pursuing a PhD in either theoretical computer science or graph and network visualization. We seek PhD
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Description The Mathematics department at the University of Luxembourg currently has openings for up to 4 PhD positions in the following areas: - Algebraic geometry, - Geometry, - Mathematical aspects
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Theory Algebraic Geometry Arithmetic Geometry Derived Categories Langlands Program Geometric Representation Theory In addition, relevant teaching experience is expected, and the successful candidate will
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About the Role The project “An Erlangen Programme for AI” (funded by the UKRI), will broadly involve applying advanced mathematical techniques for understanding training in neural networks, with