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Field
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availability. We seek candidates with outstanding research achievements and potential, as well as a strong commitment to teaching. Candidates must have a PhD and an record in research and publication. A
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methods for differential equations and scientific machine learning; geometric deep learning, manifold learning, equivariant methods, high-dimensional geometry, and structure-aware learning; and the
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Position Description The Department of Mathematics at the University of Luxembourg is offering a doctoral researcher position in differential geometry. The contract is for 3 years; extension to 4
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methods, high-dimensional geometry, and structure-aware learning; and the mathematical foundations of trustworthy AI, including stability, robustness, verification, interpretability, and generalization
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algebra, analysis, applied mathematics, and geometry/topology. Candidates must submit a cover letter and curriculum vitae at https://www.ubjobs.buffalo.edu/postings/64020 Candidates should also submit a
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, applied mathematics, and geometry/topology. Candidates must submit a cover letter and curriculum vitae at https://www.ubjobs.buffalo.edu/postings/64013 Candidates should also submit a cover letter
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across the arts, humanities, social sciences and natural sciences. Qualifications Required Qualifications: PhD degree or foreign equivalent in geometry or closely related field, with an established record
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Job description We invite applications for a fully funded PhD position in the area of Scientific Machine Learning (SciML), which integrates data-driven machine learning techniques with established
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modern machine learning, with broad implications for both theory and applications. The PhD project focuses on the theoretical foundations and algorithmic design of discrete gener- ative models, situated
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reproducible fabrication, assembly and quality-control procedures using the cleanroom facilities at the London Centre for Nanotechnology. You will characterise device geometry, porosity, surface chemistry