جست‌وجوی استاد و پژوهشگر

• Nonlinear Partial Differential Equations
• Cross-Diffusion Systems
• Mathematical Modeling in Semiconductor Devices
• +۵ مورد دیگر

Ansgar Jüngel is a Full Professor for Analysis of Nonlinear Partial Differential Equations at TU Wien, Austria. He leads the Institute of Analysis and Scientific Computing within the Faculty of Mathematics and Geoinformation. His research focuses on mathematical modeling, analysis, and numerical simulation of complex systems, particularly in semiconductor physics, quantum hydrodynamics, and biological transport phenomena. Jüngel holds a PhD from TU Berlin (1994) and habilitation (1997), with prior academic roles at University of Konstanz and University of Mainz. His work spans nonlinear PDEs, cross-diffusion systems, entropy methods, and applications in semiconductor modeling (e.g., quantum drift-diffusion, Maxwell-Stefan systems). Notable contributions include rigorous derivation of energy-transport models, analysis of quantum fluid models, and development of entropy-dissipative numerical schemes. Awards include the ERC Advanced Grant (2021) and Tsungming-Tu Award (2011). Teaching emphasizes advanced mathematical topics: cross-diffusion systems, partial differential equations, and computational finance. Active in PhD supervision and collaborative projects, he co-authored books on transport equations, entropy methods, and semiconductor modeling. His team includes postdocs and PhD students working on topics like multi-species populations, tumor growth modeling, and stochastic cross-diffusion systems.

Andreas Buttinger-Kreuzhuberمشاهده پروفایل

پژوهشگر

• Hydrology
• Computational Fluid Dynamics
• Environmental Engineering
• +۳ مورد دیگر

Andreas Buttinger-Kreuzhuber is affiliated with the Department of Engineering Hydrology at TU Wien. His research focuses on advanced hydrodynamic modeling, GPU-accelerated flood simulations, and integrating geospatial data for flood risk assessment. He contributed to projects like HORA 3.0, a national flood risk mapping initiative, and developed frameworks for high-resolution simulations of urban and rural flash floods. His work bridges computational science and environmental engineering, emphasizing real-world applications in disaster management and urban planning. Education: Dipl.-Ing. (FH) in Civil Engineering, Dr.techn. (PhD) in Technical Sciences, and BSc in a related field. Notable research themes include shallow-water schemes optimization, interactive visualization tools for flood management, and nanopore fluid dynamics. His interdisciplinary approach combines hydraulic modeling with geomatics and computational fluid dynamics. Collaborations include work with Prof. Günter Blöschl and the Network Lab at TU Wien. Recent efforts focus on accelerating hydrodynamic simulations using GPU technology and improving regional-scale flood modeling accuracy.

Elisa Davoliمشاهده پروفایل

استاد

• Calculus of Variations
• Micromagnetics
• Solid-Solid Phase Transitions
• +۴ مورد دیگر

Elisa Davoli is a Professor at TU Wien, affiliated with the Multiscale Calculus of Variations Research Group (E101-01-3). Her work focuses on calculus of variations, micromagnetics, and material science, with applications in phase transitions, nonlocal models, and stochastic homogenization. She collaborates extensively with researchers such as Irene Fonseca, Manuel Friedrich, and Ulisse Stefanelli. Her recent research includes studies on fractional Cahn-Hilliard systems, sharp-interface limits, and optimal control in nonlocal frameworks. She also investigates stochastic homogenization in micromagnetics and structural changes in nonlocal denoising models through bilevel learning. Key contributions include existence results in large-strain magnetoelasticity, two-scale convergence methods for composite materials, and the derivation of linearized fracture models under non-interpenetration constraints. Her work bridges mathematical analysis with applications in solid mechanics and image processing.

• Topology Optimization
• Partial Differential Equations (PDEs) on Surfaces
• Shape Optimization
• +۳ مورد دیگر

Kevin Sturm is an Associate Professor at TU Wien's Department of Numerical Analysis. His research focuses on topology optimization, partial differential equations on surfaces, and their applications in engineering and biomedical contexts. He leads projects involving topological derivatives, shape optimization algorithms, and numerical methods for complex systems. Key research interests include developing automated computational techniques for topological sensitivity analysis, with applications in nonlinear elasticity, reaction-diffusion systems, and structural mechanics. His work bridges theoretical mathematics (e.g., variational inequalities) with practical engineering problems (e.g., actuator design, membrane mechanics). Recent publications highlight advancements in Lagrangian-based optimization frameworks, adjoint methods for high-order derivatives, and numerical implementations in software like NGSolve. He advises graduate students Albert Vlasak and Philipp Wörle on shape optimization for parabolic problems and variational inequalities. His lab contributes to interdisciplinary projects at the intersection of computational mathematics, material science, and biomedical engineering. Current efforts focus on optimizing geometries for vibration control, membrane behavior, and imaging modalities like electrical impedance tomography.

• Lorentzian Geometry
• General Relativity
• Differential Geometry
• +۳ مورد دیگر

Clemens Sämann is an associate professor at the Department of Mathematics, Faculty of Mathematics, University of Vienna. His research focuses on Lorentzian geometry, general relativity, and differential geometry, with significant contributions to spacetime structures, geodesic analysis, and curvature bounds. Research Interests: Lorentzian geometry and causality Geodesic completeness in non-smooth spacetimes Curvature theorems for Lorentzian length spaces Impulsive gravitational waves with cosmological constant Λ Recent Publications (2024-2016): His work spans timelike curvature bounds, Hausdorff measures in Lorentzian contexts, and mathematical models of spacetime singularities. Key trends include causal differential calculus, metric splitting, and geometric analysis of gravitational wave interactions.

Manuela Ganderمشاهده پروفایل

دانشیار

• Clinical Psychology
• Attachment Theory
• Personality Disorders
• +۴ مورد دیگر

Associate Professor Manuela Gander is a faculty member at the University of Innsbruck's Department of Clinical Psychology II. Her office is located at Universitätsstraße 5-7 (Room 3S12), and she can be contacted via email at Manuela.Gander@uibk.ac.at or by phone at +43 (0)512 507-56016. Consultation hours are available by appointment. Her research integrates clinical psychology with neurobiological approaches, focusing on: Attachment theory applied to adolescent psychopathology Personality disorders and their neurostructural correlates in eating disorders Trauma processing in vulnerable populations (e.g., refugees) Gender dysphoria and identity development in youth Neuroimaging biomarkers for treatment response Recent publications (2023-2025) demonstrate strong emphasis on longitudinal neuroimaging studies of anorexia nervosa, attachment-related neuroplasticity, and transdiagnostic approaches to adolescent personality pathology. Over 50% of her recent work investigates neurobiological mechanisms underlying therapeutic outcomes. She supervises scientific work but no specific student names or awards are mentioned in available sources.

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +۳ مورد دیگر

Herbert Egger is a Professor of Numerical Analysis and Scientific Computing at Johannes Kepler University (JKU) Linz and heads the Institute of Numerical Mathematics. He is also a Group Leader at RICAM (Johann Radon Institute for Computational and Applied Mathematics) and Scientific Director at the Austrian Academy of Sciences. His research focuses on advanced numerical methods for partial differential equations, including structure-preserving discretizations, inverse problems, and computational electromagnetics. Current projects include SFB F90-N (CREATOR) for electric machine co-simulation, SPP 2256 for phase-field modeling in additive manufacturing, and TRR 146 on multiscale soft matter systems. Key research areas: mixed/hybrid finite element methods, energy-based modeling, model order reduction, radiative transfer, and inverse problem stabilization. His recent work emphasizes nonlinear magnetostatics, phase separation models, and efficient solvers for time-dependent systems. Egger collaborates internationally with institutions like TU Eindhoven and TU Darmstadt.

Olga Mula Hernandezمشاهده پروفایل

استاد

• Numerical Analysis of Partial Differential Equations
• Mathematical Foundations of Scientific Machine Learning
• High-Dimensional and Nonlinear Approximation
• +۲ مورد دیگر

Olga Mula Hernandez is a Professor of Mathematics at the University of Vienna since September 2025, holding the Chair of Computational Partial Differential Equations. Previously, she served as an Associate Professor at Eindhoven University of Technology (2022-2025) and Assistant Professor at University Paris-Dauphine (2015-2022). Her educational background includes a double master's degree in Applied Mathematics and Nuclear Engineering from École Polytechnique (France) and Escuela Politécnica (Madrid) (2005-2011), followed by a PhD in Applied Mathematics from Sorbonne University (2011-2014). She also completed a postdoctoral fellowship at RWTH Aachen (2014-2015). Professor Mula's research centers on numerical analysis of partial differential equations with data-driven integration. Her work spans mathematical foundations of scientific machine learning (including PINNs), high-dimensional nonlinear approximation for PDE solvers, data assimilation and inverse problems, numerical optimal transport, and structure-preserving numerical schemes. She develops explainable, data-efficient computational methods that leverage physical insights rather than relying on black-box machine learning approaches. Her methodologies address critical applications across 3D-printing, haemodynamics, environmental pollution modeling, epidemiology, and nuclear engineering, where accurate physics-based simulations are essential for real-world problem solving.

David Fajmanمشاهده پروفایل

دانشیار

• General Relativity
• Cosmology
• Gravitational Waves
• +۳ مورد دیگر

David Fajman is an Associate Professor at the University of Vienna, where he leads the Gravitational Physics group and serves as Head of Gravitational Physics. His research centers on Einstein's field equations, gravitational waves, and cosmological structures, blending mathematical physics with geometric analysis to study spacetime dynamics and relativistic matter. Research interests include: Dynamics of spacetime geometry under gravitational forces Formation and stability of cosmological structures Relativistic fluid mechanics in expanding universes Mathematical analysis of black holes and singularities Asymptotic behavior of solutions to Einstein's equations His recent publications focus on spacetime stability, cosmological fluids, and black hole physics, with emphasis on analytical techniques in general relativity and cosmology. Key trends include mathematical investigations of spacetime asymptotics, singularity formation, and stability thresholds in relativistic systems. Research Projects: Geometric Transport equations and the nonvacuum Einstein flow (FWF, 2017–2021) Relativistic fluids in cosmology (FWF, 2021–2024) Matter dominated cosmology (FWF, 2025–present) He advises PhD student Liam Urban and collaborates with postdocs Adam Cieślik and Maximilian Ofner. Former group members include Gernot Heißel, Zoe Wyatt, and Hamed Barzegar.

Maximilian Ofnerمشاهده پروفایل

پژوهشگر

• Geometric Analysis
• Mathematical Cosmology
• Partial Differential Equations
• +۳ مورد دیگر

Maximilian Ofner is a Researcher in the Gravitational Physics group at the Faculty of Physics, University of Vienna, currently funded by the Austrian Science Fund (FWF) project "Relativistische Fluide in der Kosmologie" under David Fajman's supervision. His work focuses on mathematical general relativity and cosmological modeling. Education: Bachelor of Science (Physics) at the University of Vienna (2019) Master of Science (Physics) at the University of Vienna (2021) with thesis on "Future Stability of Relativistic Fluids and Pressureless Dust in Expanding FLRW-type Models" (advisor: David Fajman) Research Focus: His work centers on geometric analysis of Einstein's equations, particularly stability properties of relativistic fluids and dust in expanding cosmological spacetimes. He investigates partial differential equations on curved manifolds to understand structure formation, shock dynamics, and long-term cosmic evolution using rigorous mathematical frameworks. Publication Trends: Recent publications (2021-2024) reveal a progression from dust spacetime stability to complex fluid dynamics, with increasing focus on phase transitions and shock formation thresholds. His research consistently bridges theoretical general relativity, differential geometry, and nonlinear PDE analysis in cosmological contexts. Scientific Recognition: VDSP Mobility Fellowship (2022) Honors in Masterclass Mathematical Physics Vienna (2021) Grants and Collaborations: Supported by FWF funding, he actively collaborates with international researchers including David Fajman, Zoe Wyatt, and Todd Oliynyk. His participation in workshops at Mittag Leffler Institute, Erwin Schrödinger Institute, and University College London demonstrates strong integration into the mathematical relativity community. Research Environment: Based in Vienna's Gravitational Physics group, he contributes to Austria's growing expertise in mathematical cosmology through seminars, international conferences, and specialized summer schools focused on nonlinear PDEs and general relativity.

• Global Optimization
• Numerical Analysis
• Data Analysis
• +۳ مورد دیگر

Waltraud Huyer is a faculty member at the Department of Mathematics, Faculty of Mathematics, University of Vienna. Her office is located in Room 04.120 at Oskar-Morgenstern-Platz 1, 1090 Wien, Austria. She actively teaches courses including Numerical Mathematics and Introduction to Mathematics for both undergraduate and secondary school teacher accreditation programs. Her research spans Global and Local Optimization , Numerical Analysis , Data Analysis , Protein Folding , and Population Dynamics with structured populations. She develops advanced optimization algorithms like SNOBFIT for noisy environments and MINQ8 for quadratic programming, while applying mathematical techniques to biological problems such as protein structure prediction and age-structured population modeling. Analysis of her 14 most recent publications (1994-2018) reveals three dominant research thrusts: (1) Algorithmic development in global optimization (7 papers), including multilevel coordinate search and exact penalty functions; (2) Mathematical biology applications (5 papers), particularly in protein folding and population dynamics; (3) Computational verification methods (2 papers) for linear systems and feasibility problems. Her work consistently bridges theoretical mathematics with practical computational implementations. Her scientific contributions include widely-used algorithms implemented in MATLAB, such as the MCS global optimizer and SNOBFIT for noisy optimization, available through the University of Vienna's software repository. Huyer supervises teaching activities for foundational mathematics courses, emphasizing practical computational skills using MATLAB for numerical analysis and regression problems. Her teaching materials include detailed exercise sets with structured assessment criteria requiring both theoretical understanding and programming implementation.