Prof. Dr.-Ing. Werner Seim serves as Section Head for the Department of Building Rehabilitation and Timber Engineering at the University of Kassel's Faculty of Civil and Environmental Engineering. His research focuses on seismic-resistant timber structures, advanced fabrication techniques, and adhesive-bonded composites. Teaches courses: Construction I/II, Introduction to Timber Construction, Structural Design Leads projects: HOCHHINAUS (high-rise timber), SafeTeCC (adhesive bonding), wood-textile composites Specializes in: CLT shear walls, dowel connections, earthquake engineering Research trends from 2013-2025 emphasize timber structural mechanics, seismic performance, hybrid composites, and computational fabrication methods. His work addresses both historical preservation and modern sustainability in timber construction. Current affiliations include: University of Kassel Faculty of Civil and Environmental Engineering Building Rehabilitation and Timber Engineering Department
Miles Stoudenmire is a Researcher at the Flatiron Institute's Center for Computational Quantum Physics (CCQ) , joining in 2017. He holds a Ph.D. in Physics from the University of California Santa Barbara (2010) and a B.S. in Physics and Math from the Georgia Institute of Technology (2005). His work focuses on tensor networks and their applications in quantum mechanics and machine learning. Education: Ph.D. in Physics, University of California Santa Barbara (2010) B.S. in Physics and Math, Georgia Institute of Technology (2005) Stoudenmire specializes in enhancing tensor network methods to model realistic quantum systems, including finite temperature effects and chemically accurate basis sets. He has developed the ITensor software library, which enables efficient transcription of tensor network diagrams into code for algorithm prototyping. His research bridges quantum physics and computational techniques, with applications in condensed matter physics and quantum computing. Key publication trends include advancements in density matrix renormalization group (DMRG) methods, quantum-inspired machine learning algorithms, and topological quantum simulations. Affiliations: Flatiron Institute (2017–present) University of California Irvine (postdoctoral) Perimeter Institute for Theoretical Physics (research scientist)
Dr.-Ing. Martin Heinrich is a researcher at the Institute of Mechanics and Fluid Dynamics within the Faculty of Mechanical, Process and Energy Engineering at Technical University Bergakademie Freiberg . His work focuses on numerical flow simulation and computational fluid dynamics. Education: Diploma in Mechanical Engineering (2011), TU Bergakademie Freiberg (grade: 1.3) Doctorate in Mechanical Engineering (2016), TU Bergakademie Freiberg (summa cum laude) Martin Heinrich's research spans numerical simulation of multiphase flows , atomization of molten metals and water jets , and surface structuring using laser beams . He has conducted research stays at the University of Toronto (2019, 2017) and Amirkabir University of Technology (2018). His 15 most recent publications (2013–2025) include work on air sampling in closed environments , fluid-structure interaction , CFD software development , and laser-induced surface structuring , with a focus on computational methods like OpenFOAM and ANSYS CFX. Key awards: Summa cum laude doctorate in Mechanical Engineering (2016) He has collaborated with researchers such as Prof. Rüdiger Schwarze , Prof. Kinnor Chattopadhyay , and Dr. Hossein Khaleghi . His work includes grants for multi-scale modeling and fluid dynamics software tools .
Annabelle Bohrdt is a Professor at the University of Regensburg's Institute of Theoretical Physics and an MCQST START Fellow at LMU Munich. She concurrently holds a postdoctoral fellowship at Harvard University and ITAMP. She leads the Theory of Correlated Matter and Quantum Data Group , focusing on quantum many-body systems, machine learning applications in physics, and quantum simulation. Education: PhD from Technical University of Munich (with research exchange at Harvard University); Master's/Diploma from Technical University of Kaiserslautern. Research Interests: Her work integrates numerical methods, machine learning, and quantum simulation to study strongly correlated systems. Key areas include: Neural quantum states and machine learning for quantum data analysis Fermi-Hubbard and t-J models in mixed dimensions Exotic quantum phases (stripes, skyrmions, fractionalization) Quantum state tomography and Hamiltonian reconstruction techniques Non-equilibrium dynamics in optical lattices Publications Focus: Her recent articles (2023–2024) predominantly explore neural network applications in quantum physics, doped antiferromagnets, lattice gauge theories, and quantum simulation protocols. A strong emphasis on machine learning–quantum physics intersections is evident, with innovations in interpretable AI for quantum data. Awards & Honors: Friedrich Hirzebruch-Promotionspreis (German National Academic Foundation Thesis Prize) Finalist for Deborah Jin Thesis Award (APS DAMOP 2022) MCQST START Fellowship Advising & Team Leadership: Directs 13+ students (PhD/Master's/Bachelor's) at Universität Regensburg. Current research group includes projects on neural quantum states, Fermi-Hubbard models, and quantum data analysis. Actively recruits students for quantum many-body physics projects. Teaching: Courses include Numerical Methods for Quantum Many-Body Systems (Winter 2023/24) and Machine Learning for Quantum Many-Body Physics (Summer 2023), blending theory with hands-on coding and research applications.
Dr. Andreas Schäfer is a researcher at the Geophysical Institute (GPI) of Karlsruhe Institute of Technology (KIT), specializing in natural hazard risk assessment and disaster forensics. He leads research on tsunami, earthquake, and flood risks through the CEDIM Forensic Disaster Analysis Group, producing rapid-impact reports for global events like the 2023 Türkiye earthquakes and 2025 Pacific tsunamis. Research Focus: Schäfer's work integrates geophysics, machine learning, and multi-disciplinary analysis to address: Tsunami generation mechanisms and coastal risk modeling Earthquake engineering and forecasting using statistical and computational methods Climate-extreme impacts on flood and heatwave vulnerabilities Real-time disaster forensics for policy-relevant risk reduction Publication Trends: His recent articles demonstrate a focus on forensic disaster analysis, climate-related hazard amplification, and machine learning applications in geophysics. Collaborative works frequently appear in multi-disciplinary journals like Natural Hazards and Earth System Sciences . Academic Engagement: Teaches courses in seismological signal processing, seismic wave theory, and engineering geophysics at KIT. No named students or awards are documented in available materials. Affiliations: Core member of CEDIM Forensic Disaster Analysis Group, conducting rapid damage assessments for global disasters since at least 2017.
Dr. Christina Radlbeck is a Researcher at the Department of Metal Construction within the TUM School of Engineering and Design at Technical University of Munich, working under Professor Martin Mensinger. She has been with the department since completing her doctorate in 2006, establishing herself as a specialist in metal construction with particular expertise in aluminum structures and bridge engineering. Her academic background includes: 1996-2000: Diploma in Civil Engineering, Technical University of Munich 2001: Research Engineer at Department for Civil Engineering and Applied Mechanics, McGill University 2006: Doctorate (Dr.-Ing.) at Technical University of Munich with thesis 'Ganzheitliche Analyse und Bewertung von tragenden Aluminiumkonstruktionen' Dr. Radlbeck's research focuses on critical areas of structural engineering including aluminum structures, fatigue analysis, and historical steel bridge assessment. Her work bridges fundamental material science with practical structural applications, particularly in evaluating the load-bearing capacity of aluminum joints and determining safe operating intervals for historical steel bridges. She has made significant contributions to standards development, particularly regarding DIN EN 1999-1-3 for aluminum structures. Analysis of her recent publications (2023-2025) reveals three dominant research themes: (1) advanced material characterization for additive manufacturing in construction, (2) fracture mechanics applications for railway bridge safety assessment, and (3) fatigue behavior analysis of novel aluminum and stainless steel alloys. Her work consistently connects material properties with structural performance, addressing both traditional construction challenges and emerging technologies. As an educator, Dr. Radlbeck teaches specialized courses including 'Assessment and Preservation of Historical Steel Structures,' 'Construction with Aluminum,' and 'Fracture Mechanics and Fatigue,' reflecting her deep expertise in metal construction. She has maintained an active industry presence since 2003 through independent civil engineering work, ensuring her research remains grounded in practical engineering challenges. Her research collaborations primarily involve colleagues at TUM's Department of Metal Construction, particularly with Dorina Siebert, Jakob Blankenhagen, and Professor Martin Mensinger, contributing to numerous publications in high-impact journals and international conferences. These collaborations focus on advancing sustainable practices in structural engineering through innovative assessment methods and new construction technologies.
Nadine Thomas (M.Sc.) is a Researcher at the Chair of Metal Construction at the Technical University of Munich since 2017. She holds degrees in Civil Engineering from OTH Regensburg (B.Eng., 2014) and Technical University of Munich (M.Sc., 2017). Her work focuses on structural stability, particularly buckling analysis under multiaxial stresses and eccentric load introduction in steel and composite constructions. Education B.Eng., Civil Engineering, OTH Regensburg (2014) M.Sc., Civil Engineering, Technical University of Munich (2017) Her research addresses critical challenges in bridge engineering, fire protection, and sustainability, with key contributions to Eurocode 1993-1-5 compliance. She has collaborated on studies involving historical steel structures, elastomeric bearings, and additive manufacturing in metal construction. Publications span journals like Stahlbau and conferences including the Japanese-German Bridge Symposium. Recent work includes assessments of the Chemnitz Viaduct's bearings and torsional stiffness effects on longitudinally stiffened plates. Collaborations involve Prof. Martin Mensinger, Joseph Ndogmo, and other researchers. No scientific awards are mentioned in the provided text.
Bastian Devresse is a Researcher at the Chair of Structural Analysis at the Technical University of Munich (TUM) , where he has worked since 2022. He holds a Master's degree in Civil Engineering from TUM (2019-2022) and a Bachelor's degree in Civil and Environmental Engineering from Hamburg University of Technology (2015-2019). His research focuses on advanced shape optimization techniques, particularly in structural mechanics and wind engineering. Research Interests : Node-based shape optimization Isogeometric analysis and B-Rep modeling Wind-induced vibrations and flexible membrane structures Lightweight and additive manufacturing optimization Multidisciplinary optimization frameworks Publications highlight his work in developing innovative parameterizations for bead-like features, applying Vertex Morphing methods to thin-walled structures, and integrating sensitivity filtering for robust optimization. Recent work extends to wind engineering simulations and membrane wing design. Teaching includes courses on finite element methods and nonlinear structural analysis. He has supervised theses on topics like robust shape optimization and isogeometric subdivision surfaces. Contact: bastian.devresse@tum.de | Office: Room 0101.Z1.019
Guillermo Martínez-López is a Researcher at the Chair of Statics and Dynamics, Technical University of Munich (TUM), since 2024, previously working at the Chair of Statics (2020-2024). His research focuses on computational structural mechanics with applications in wind engineering and civil infrastructure design. He holds a Master of Science (2017-2019) and Bachelor of Science (2013-2017) in Civil Engineering from Universitat Politècnica de València, with study periods at RWTH Aachen (2016-2017) and KTH Royal Institute of Technology (2018-2019). His research spans Wind Engineering , Structural Optimization , and Computational Mechanics , addressing critical challenges in long-span bridge aerodynamics and membrane structure design. Key contributions include flutter mitigation strategies for cable-supported bridges and standardized pressure mapping for membrane roof canopies, emphasizing computational efficiency through forced-motion simulation optimization. Analysis of his 2019-2024 publications reveals a concentrated focus on wind-structure interaction problems, with increasing emphasis on standardization methodologies for membrane structures and computational cost reduction in aerodynamic simulations. His work bridges theoretical computational mechanics with practical civil engineering applications. His scientific recognition includes: La Caixa Foundation Research Fellowship (2020-2022) DAAD Research Fellowship (2020) He actively contributes to third-party funded projects including CoDA, MistralWind, WINSENT, and FlexWing, focusing on wind engineering applications and structural optimization. As an instructor in Wind Engineering courses at TUM, he integrates research into teaching while collaborating within Prof. Wüchner's research group on advanced computational methods. His work is embedded within TUM's computational mechanics ecosystem, contributing to software development (Kratos Multiphysics) and participating in interdisciplinary teams addressing wind effects on civil structures through projects like Digitaler Baukasten.
Dr. Saeed Mahmoodpour is a researcher at the Technical University of Munich's Department of Geothermal Technologies within the School of Engineering and Design. He works under the Assistant Professorship of Geothermal Technologies led by Prof. Dr. Michael Drews, focusing on subsurface energy systems and contributing to projects like the Geothermal Alliance Bavaria (GAB) and GoEffective. His educational background includes: Bachelor of Science in Petroleum Engineering from Sharif University of Technology (2008-2012) Master of Science in Reservoir Engineering from Sharif University of Technology (2012-2014) Professional experience at University of Tehran and Technical University of Darmstadt prior to his current position Dr. Mahmoodpour's research centers on subsurface energy systems , with particular expertise in fracture network modeling , THMC simulations , carbon capture and storage , and underground hydrogen storage . His work integrates computational approaches with geological analysis to address challenges in sustainable energy development. He has developed sophisticated models for predicting behavior of geological formations under various energy storage and extraction scenarios. Analysis of his recent publications (2022-2025) reveals a strong focus on hydrogen storage in geological formations and enhanced geothermal systems . His research demonstrates increasing sophistication in modeling coupled physical processes, with recent work incorporating molecular dynamics simulations alongside traditional reservoir modeling approaches. A notable trend is his expanding investigation of CO2-hydrogen mixtures and their behavior in subsurface environments. Dr. Mahmoodpour actively collaborates with researchers across multiple institutions, contributing to the Geothermal Congress and EGU General Assembly presentations. His work supports the Bavarian Pressure Map initiative and other regional geothermal development efforts in the North Alpine Foreland Basin.
Nikolaus Adams is a Professor at the Chair of Aerodynamics and Fluid Mechanics at the Technical University of Munich (TUM) . His research focuses on computational fluid dynamics (CFD), numerical methods, and data-driven modeling of complex fluid phenomena. Key contributions include the development of differentiable CFD frameworks like JAX-Fluids and integration of machine learning with high-order schemes. Research Interests : High-order numerical methods (WENO, SPH, Lattice-Boltzmann) Machine learning/RL for flow control and turbulence modeling Quantum algorithms for fluid simulations Multiphase flows with surface tension and cavitation Shock wave interactions and aerodynamic breakup Recent article trends highlight applications of differentiable programming, neural networks, and Bayesian optimization in compressible/two-phase flows, alongside quantum lattice-Boltzmann advancements. Labs/Teams : Leads the Chair of Aerodynamics and Fluid Mechanics at TUM, contributing to the TUMWAER group and TUZEMSE research initiatives.
Yang Tao, Ph.D. , a Marie Skłodowska-Curie Postdoctoral Fellow at the Technical University of Munich (TUM), works at the Chair of Acoustics of Mobile Systems under Prof. Steffen Marburg. His research focuses on developing textile-based metamaterials for low-frequency noise absorption, bridging acoustics, material engineering, and computational modeling. Acoustic metamaterials Fibrous material characterization Numerical acoustics Multi-functional textiles Recent publications highlight his expertise in Johnson-Champoux-Allard-Lafarge models for fibrous materials, acoustic bound states, and modular multilayer systems. Yang's work addresses urban noise challenges through scalable textile manufacturing techniques. Scientific awards include the prestigious Marie Skłodowska-Curie Postdoctoral Fellowship . His research integrates experimental and computational approaches to advance sound absorption and material design.
Dr. Thomas Schulte-Herbrüggen is a researcher at the Department of Chemistry , Technical University of Munich , with a focus on quantum systems theory and control. His work bridges mathematical physics and quantum information processing. Research Areas: Mathematical Systems and Control Theory, Optimal Quantum Control, Open Quantum Systems, Lie Groups, Numerical Ranges. His publications span quantum state transfer, error correction, and cavity QED, emphasizing optimal control methods and symmetry principles. In 2016, he presented at conferences in Paris and Berlin on quantum dynamics and open systems.
Itay Hen is an Associate Professor of Research in the Department of Physics and Astronomy and Principal Scientist at the Information Sciences Institute (ISI), University of Southern California. He has held research faculty positions at USC since 2013, progressing from Assistant Professor (2016-2020) to his current Associate Professor role since 2020, while leading ISI's quantum computing initiatives. His educational background includes dual bachelor's degrees in Physics and Psychology from Tel Aviv University, followed by a Ph.D. in Physics from the same institution in 2009. Postdoctoral training included theoretical condensed matter research at Georgetown University and UC Santa Cruz, plus a senior scientist role at NASA Ames Research Center within the Quantum Artificial Intelligence Laboratory—a NASA/Google/USRA collaboration. Dr. Hen's research centers on Quantum Computing and Computational Physics , with specific expertise in gate-based quantum simulation algorithms, quantum annealer limitations, and methods for studying equilibrium/non-equilibrium properties of strongly correlated quantum systems. His work bridges theoretical frameworks with practical quantum hardware applications. Analysis of his 15 most recent publications (2017-2025) reveals consistent focus on quantum algorithms and Monte Carlo techniques, with accelerating output in 2024. Key themes include Feynman path integrals, spin/Bose-Hubbard model simulations, and quantum spectrum estimation, primarily published in Physical Review journals and Quantum. He leads the Hen Lab at USC's ISI, which operates within the Quantum Artificial Intelligence Laboratory framework. His research has been supported through NASA/Google/USRA collaborations focused on quantum optimization for complex computational problems, though specific grant details and student mentorship records aren't provided in the source material.
Johannes Schmid, M.Sc., is a Research Associate at the Chair of Vibroacoustics of Vehicles and Machines at the Technical University of Munich . His work focuses on integrating machine learning with computational acoustics, particularly in physics-informed deep learning and uncertainty quantification for vibroacoustic systems. Research Interests Physics-informed deep learning for acoustic modeling Data-driven surrogate modeling for dynamic systems Uncertainty quantification in industrial applications Stochastic Finite Element Methods Interactive acoustics apps for education Publications Schmid has authored/co-authored over 15 publications in computational acoustics, with recent work on neural networks for boundary integral methods, metamaterial design, and hybrid machine learning techniques. His research spans automotive applications, noise control, and educational tools. Laboratory Affiliation He is affiliated with the Chair of Vibroacoustics of Vehicles and Machines , contributing to projects on vehicle acoustics, fluid-structure interaction, and deep learning applications in engineering.
