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wish to shape the future direction of archival studies through innovative, theory-driven, and practice-oriented research in an optimal research environment. Close cooperation with Austrian and
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data, stochastic optimization, modern machine learning methods, scalable algorithms for advanced Machine Learning techniques and explainable AI. In teaching, the position will contribute, inter alia
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techniques. The ideal candidate will have prior exposure to modern developments in at least one of these fields: Optimal Transport, numerical analysis of PDEs, functional analysis, mathematical foundations
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to invite applications for a University Assistant (Praedoc / PhD Candidate) to join the research team led by Univ. Prof. Olga Mula. Our group’s work sits at the forefront of numerical analysis for Partial