Paula Hilbert is a PhD student and University Assistant at TU Wien's Institute of Analysis and Scientific Computing, supervised by Prof. Dirk Praetorius. Her research focuses on Numerics of PDEs, adaptive FEM, and contractive iterative solvers, with current emphases on Finite Element Methods, Multigrid methods, and optimal preconditioners for Galerkin matrices. She teaches the course 'Introduction to Programming' (4 VU) in the summer term of 2025. Education: BSc (1998) and MSc (2022) in Technical Mathematics from TU Wien. Current affiliation includes the ASC-Nextcloud and TU-Webmail platforms. No awards or grants are explicitly listed in the provided materials.
Maximilian Brunner is a Postdoc at TU Wien's Institute of Analysis and Scientific Computing (E 101), specializing in Numerics of Partial Differential Equations (PDEs). He holds a Dipl.-Ing. (MSc) and Dr.techn. (PhD) from TU Wien, with his doctoral thesis on On optimal adaptivity for semilinear PDEs earning the 2025 Study Prize from the Austrian Mathematical Society (ÖMG). His research focuses on adaptive finite element methods (FEM), iterative solvers for nonlinear PDEs, and cost-optimality analysis. He collaborates with Prof. Dirk Praetorius and contributes to projects funded by the Austrian Science Fund (FWF). Previously, he was a PhD student (2020–2024) and taught courses in numerical mathematics and scientific programming. His work bridges theoretical advancements in adaptive algorithms with practical computational efficiency, addressing challenges in nonsymmetric and semilinear PDEs. Education: PhD in Technical Mathematics, TU Wien (2024), supervised by Dirk Praetorius MSc in Technical Mathematics, TU Wien (2020), supervised by Winfried Auzinger Research Interests: His interdisciplinary work centers on developing adaptive FEM strategies for cost-optimal solutions to PDEs. Key areas include: Contractive iterative solvers for nonlinear systems Goal-oriented error estimation for targeted accuracy Quasi-optimal computational cost in adaptive algorithms His methods prioritize minimizing computational resources while maintaining solution fidelity, with applications to elliptic and semilinear PDEs. Awards: 2025 Study Prize (ÖMG) for PhD thesis Grants & Funding: FWF Projects P33216 and P28367 Vienna School of Mathematics membership (2020–present) Labs/Teams: Core member of the Numerics of PDEs research group, part of TU Wien's ASC unit. Active in collaborative projects with international conferences and workshops.
