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, geometric deep learning. Considered an advantage: experience in programming or course work in computer science, algebra, topology or differential geometry, knowledge of topological data analysis or machine
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or course work in computer science, algebra, topology or differential geometry, knowledge of topological data analysis or machine learning will also be a benefit. Qualifications and personal qualities
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The Centre for Quantum Mathematics (QM) at the Department of Mathematics and Computer Science, University of Southern Denmark (SDU) invites applications for a number of 2-3 year positions as
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Job Description The Centre for Quantum Mathematics (QM) at the Department of Mathematics and Computer Science, University of Southern Denmark (SDU) invites applications for a number of 2-3 year
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fellowships are intended to allow the postholder to develop their own independent research programme in any area, building on; or complementary to, the research centre's existing strengths. These include
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be carring out research in tropical and computational algebraic geometry. The applicants must present a description outlining their academic interests and motivation for the PhD project. The project
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be carring out research in tropical and computational algebraic geometry. The applicants must present a description outlining their academic interests and motivation for the PhD project. The project
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 5 days ago
Information Research Fellowship (BI) Job Status Full-time Hours Per Week 37,5 Offer Starting Date 19 Nov 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
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: The research fellow will Utilize recently developed mathematical tools from algebraic topology, combinatorial topology, computational topology, and differential geometry (e.g., Betti numbers, Hodge
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geometry, and/or data science. Specific topics of focus include, but are not limited to, linear response, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics