Find Academic Supervisors

• Numerical Analysis
• Partial Differential Equations
• Scientific Computing
• +4 more

Albert Gilg is a Professor at the Technical University of Munich (TUM), affiliated with the Department of Mathematics and the TUM School of Computation, Information and Technology . His work focuses on numerical analysis and computational methods for real-world applications in science and engineering. Research Interests: Modelling and Analysis of complex systems Numerical methodologies for partial differential equations High Performance Computing (HPC) and parallel algorithms Uncertainty quantification and surrogate models Applications in fluid dynamics, biomedical engineering, and geophysics He contributes to interdisciplinary projects such as simulating cerebral aneurysms, seismic impacts on structures, and methane gas hydrate reservoirs. His team collaborates on third-party funded initiatives by the DFG, EU, and Gauss Centre for Supercomputing. Advising and Grants: Focus on numerical methods for fractional differential equations Development of energy-corrected finite elements Leadership in the Numerical Analysis research group at TUM

Mathis FrickeView profile

Researcher

• Analysis
• Fluid Dynamics
• Wetting Dynamics
• +3 more

Dr. Mathis Fricke is a researcher in the Department of Mathematics at TU Darmstadt, affiliated with the Mathematical Modeling and Analysis (MMA) Institute. His work focuses on computational fluid dynamics, wetting processes, and mathematical modeling of multiphase systems. He specializes in numerical methods such as the Volume-of-Fluid (VOF) technique and Level-Set approaches, with applications to capillary dynamics, surfactant transport, and interface tracking in complex geometries. His research bridges experimental validation with numerical simulation, addressing challenges in fluid mechanics, energy systems, and biomedical engineering. Key areas of expertise include dynamic contact angle modeling, multiphase flow simulations using OpenFOAM modules, and optimization of fluid transport in microfluidic systems. He has contributed to advancing numerical frameworks for unstructured meshes and has collaborated on projects involving Bayesian model calibration and error quantification for energy economy systems. Dr. Fricke's work is characterized by interdisciplinary collaboration, combining analytical mathematics with high-performance computing. His publications span topics such as capillary rise dynamics, tribology measurements, and eigenmode analysis of slip-induced flows. Despite no explicitly listed awards, his contributions to computational methodologies have been recognized through his active role in the MMA Institute and academic collaborations. He maintains a lab/office in L2|06 411, Peter-Grünberg-Straße 10, Darmstadt, and is accessible via fricke@mma.tu-darmstadt.de.

Stefan UlbrichView profile

Professor

• Nonlinear Optimization
• PDE-Constrained Optimization
• Optimal Control
• +3 more

Stefan Ulbrich is a Professor of Nonlinear Optimization at the Department of Mathematics, Technische Universität Darmstadt. His research focuses on optimization theory, algorithms, and applications, particularly in PDE-constrained optimization, optimal control, and numerical methods. He co-authored influential textbooks on nonlinear and PDE-constrained optimization. His work bridges theoretical foundations with practical engineering solutions, addressing challenges in fluid dynamics, electromechanical systems, and energy processes. Key contributions include robust optimization frameworks, sensitivity analysis for complex systems, and model reduction techniques. His research emphasizes interdisciplinary applications, such as electric machine design, industrial robotics, and sustainable energy systems. Research Interests: Nonlinear Optimization and Numerical Methods PDE-Constrained Optimization and Optimal Control Robust Design and Uncertainty Quantification Applications in Engineering and Energy Systems Publications: Ulbrich's work spans over 100 peer-reviewed articles and books. Notable contributions include the textbook *Nichtlineare Optimierung* (with Michael Ulbrich) and research on optimal control of fluid flows, robust optimization of electromechanical systems, and Bayesian model calibration. His recent focus includes parallel-in-time optimization, topology optimization under uncertainty, and exergy-based process optimization in green energy systems. Awards and Recognition: While specific awards are not explicitly listed, his leadership in optimization theory and impactful contributions to applied mathematics and engineering reflect sustained academic excellence.

Estela SuarezView profile

Professor

• High Performance Computing (HPC)
• Heterogeneous HPC Architectures
• Modular Supercomputing Architecture
• +4 more

Estela Suarez is a Professor of High Performance Computing at the University of Bonn's Computer Science Department and holds leadership roles at the Jülich Supercomputing Centre (JSC) in Forschungszentrum Jülich. Her work focuses on advancing modular supercomputing architectures, heterogeneous systems, and application optimization for exascale computing. She currently serves as Chair of the EuroHPC Joint Undertaking's Research and Innovation Advisory Group (RIAG) and leads the Next Generation Architectures and Prototypes research group at JSC. Education includes a PhD in Physics from the University of Geneva (2010) and a Master's in Physics (Astrophysics specialization) from Universidad Complutense de Madrid (2004). She has held senior research positions at JSC since 2010 and has led major EU-funded projects like DEEP, DEEP-ER, and DEEP-SEA. Research interests include HPC system design, hardware-software co-design, and energy-efficient supercomputing architectures. She was awarded the University of Bonn's 2023/2024 teaching award and actively contributes to initiatives like the European Processor Initiative (EPI) and the NUMERIQS consortium. Current projects include optimizing exascale systems for Earth system modeling (IFCES2), AI-driven data analytics (AIDAS), and modular supercomputing software (DEEP-SEA). During her 2024/2025 sabbatical, she is not accepting new students. Her work is published in journals like Geoscientific Model Development and conferences such as ISC and EuroHPC.

Suad JakirlicView profile

Associate Professor

• Fluid Mechanics
• Aerodynamics
• Turbulent Flow Modeling
• +4 more

Apl. Prof. Dr.-Ing. habil. Suad Jakirlic is an Associate Professor at the Technische Universität Darmstadt, affiliated with the Department of Fluid Mechanics and Aerodynamics within the School of Mechanical Engineering. His research focuses on advanced computational fluid dynamics (CFD) and turbulence modeling, particularly in complex flow configurations such as internal combustion engines, fuel cell cooling systems, and automotive aerodynamics. He specializes in Reynolds stress modeling (RSM), large eddy simulation (LES), and scale-resolving methodologies for predicting flow and thermal fields in engineering applications. He has contributed to interdisciplinary studies, including plasma-actuated flow control, thermal mixing in T-junctions, and vortex dynamics in aeronautics. His work integrates experimental validation with numerical simulations, emphasizing practical relevance in energy systems and vehicle design. Jakirlic has authored or co-authored numerous publications on turbulence modeling, flow physics, and heat transfer, often addressing industrial challenges such as engine cooling efficiency and aerodynamic noise reduction. He has also engaged in editorial roles, contributing to special issues of the International Journal of Heat and Fluid Flow (IJHFF) and recognizing peers like Professor Frank W. Schmidt and Thomas B. Gatski.

• Medical Imaging
• Positron Emission Tomography
• Magnetic Particle Imaging
• +2 more

Prof. Volkmar Schulz is a Professor at RWTH Aachen University, holding the Chair of Imaging and Computer Vision. He also serves as Principal Scientist at Fraunhofer MEVIS and is CEO/CTO of Hyperion Hybrid Imaging Systems GmbH. His career includes positions at Philips Research Laboratories and Draeger Medical. His research specializes in advanced medical imaging technologies, particularly Positron Emission Tomography (PET) and Magnetic Particle Imaging (MPI), with focus areas in detector design, image reconstruction, and system optimization. Recent work explores time-of-flight detection, depth-of-interaction measurement, and hardware innovations for improved imaging performance. Publications demonstrate his leadership in developing novel ASICs for PET systems, optimizing MPI hardware configurations, and applying machine learning for detector calibration. His work bridges fundamental physics with clinical applications in cancer diagnosis and treatment monitoring. He leads research teams at RWTH Aachen and Fraunhofer MEVIS, focusing on translating imaging innovations into clinical practice through industry collaborations.

Andrei VescanView profile

Professor

• Compound Semiconductors
• 2D Materials (TMDCs)
• Organic/Inorganic Hybrids
• +4 more

Prof. Andrei Vescan is a Universitätsprofessor (University Professor) at RWTH Aachen University's Department of Compound Semiconductor Technology. His research focuses on advanced semiconductor materials and optoelectronic devices, including III-nitrides, 2D materials like MoS2 and WS2, and hybrid organic-inorganic systems. Key applications include power electronics, flexible displays, solid-state lighting, and photovoltaics. His work emphasizes scalable fabrication techniques like MOCVD and CVD, as well as device integration challenges. Education and academic background: While not explicitly detailed in the provided texts, his professorship implies advanced degrees in materials science or electrical engineering. His research group investigates material growth, characterization, and device engineering. Notable areas include GaN-based power devices, memristors for neuromorphic computing, and large-area flexible electronics. Research interests span 3D/2D semiconductors, hybrid materials, and their integration into functional devices. Recent studies address surface damage reduction in GaN Schottky diodes, memristor-based artificial neurons, and optimized 2D material transfer techniques. His group's facilities include state-of-the-art equipment for nanoscale characterization and device fabrication. Publications highlight advancements in MOCVD-grown heterostructures, perovskite LEDs, and flexible photodetectors. While no specific awards are listed, his prolific publication record indicates recognition in semiconductor research. He advises on device reliability, process engineering, and scalable manufacturing of 2D material-based systems. Labs/teams: The Compound Semiconductor Technology (CST) group at RWTH Aachen University serves as his main research hub, equipped with advanced deposition and characterization tools. Collaborative projects likely involve industry partners for technology commercialization.

• Optimal Control
• Partial Differential Equations
• Numerical Analysis
• +3 more

Prof. Michael Hintermüller is a Professor at Humboldt University of Berlin, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics, specializing in Applied Mathematics. He also holds an affiliation with the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin. His research focuses on optimal control of partial differential equations (PDEs), variational inequalities, numerical analysis, and their applications in imaging and energy systems. Notable contributions include work on machine learning-informed PDEs, stochastic optimization, and mathematical modeling of coupled systems like hydrogen-electric markets. His recent publications emphasize data-driven methods in imaging, regularization techniques, and optimization algorithms for complex systems. He explores interdisciplinary topics such as quantum systems simulation, gas network control, and energy market modeling. His work often bridges theoretical analysis and computational implementation, with applications in engineering and medicine. Prof. Hintermüller’s research aligns with initiatives like MaRDI, aiming to build infrastructure for mathematical data sciences. His academic profile reflects a strong commitment to advancing applied mathematics through rigorous analysis and innovative numerical methods.

Baofang SongView profile

Professor

• Computational Fluid Dynamics
• Smoothed Particle Hydrodynamics
• Numerical Methods
• +2 more

Baofang Song is Professor of Applied Mathematics at Tianjin University, specializing in computational fluid dynamics and numerical methods. His research focuses on developing and analyzing particle-based simulation techniques. Research investigates instability mechanisms in smoothed particle hydrodynamics and fundamental turbulence processes. His work bridges theoretical and applied mathematics with engineering applications.

Olga KasianView profile

Professor

• Electrochemical Energy Conversion
• Electrocatalysis
• Materials Science
• +5 more

Prof. Olga Kasian is a Professor leading the Materials for Electrochemical Energy Conversion research group at the Department of Materials Science and Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg. Her work focuses on electrochemical energy systems, particularly understanding structure-function relationships in electrocatalysts and dynamic surface transformations during reactions. She employs advanced methods like synchrotron-based X-ray photoelectron spectroscopy and physical vapor deposition. Research interests include optimizing catalyst stability for energy conversion (e.g., oxygen evolution reactions) and developing high-throughput printing techniques for flexible perovskite photovoltaics. Her group collaborates widely, addressing challenges in renewable energy storage and material durability. Selected recent contributions span advancements in perovskite solar cell design, iridium-based nanofiber catalysts, and lithium-directed catalyst synthesis. No awards are explicitly listed, though her prolific publications reflect impactful contributions. Ongoing projects emphasize overcoming stability limits in electrocatalytic materials and scalable energy device fabrication.

Tobias LangerView profile

Researcher

• Distributed Systems
• Operating Systems
• Parallel and Distributed Processing
• +3 more

Tobias Langer is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg. His affiliation includes the Technische Fakultät and Department Informatik. He focuses on invasive computing systems, runtime support systems, and distributed architectures. Research interests include distributed systems, operating systems, parallel computing, and real-time systems. His work emphasizes runtime systems for many-core architectures, resource arbitration, and virtual shared memory solutions for MPSoCs. Recent publications explore iRTSS (invasive runtime support system) and system software for future computing architectures. His research trends address scalability, resource management, and real-time challenges in modern computing environments. No scientific awards are explicitly mentioned. He has advised multiple students on topics like OctoPOS operating system development and system monitoring units. Langer's work is affiliated with the SFB/TRR 89 Invasive Computing project and contributes to the OctoPOS kernel development. His lab is part of CS 4 at FAU, located in Room 0.041-113.

Johannes BehlView profile

Researcher

• Dependability and Security in Distributed Systems
• Cloud Computing
• Virtualization
• +3 more

Johannes Behl is a Researcher affiliated with the Institute of Operating Systems and Computer Networks at Technische Universität Braunschweig, with an office at Friedrich-Alexander-Universität Erlangen-Nürnberg's Department of Computer Science 4. His research focuses on dependability, security, and distributed systems, particularly in cloud computing and Byzantine Fault Tolerance (BFT). He has contributed to projects like TCLOUDS (Trustworthy Clouds) and REFIT (Resource-Efficient Fault and Intrusion Tolerance). Behl has published extensively on topics including high-performance BFT systems leveraging Intel SGX, consensus-oriented parallelization for multi-core environments, and decentralized quota management in cloud infrastructures. His work emphasizes scalable, fault-tolerant solutions for distributed systems and cloud-based workflows. Teaching contributions include courses on distributed systems, middleware, and operating systems at both TU Braunschweig and FAU. He has supervised multiple theses on fault-tolerant protocols, consensus algorithms, and cloud computing frameworks. His research integrates practical implementations with theoretical advancements in system dependability and resource efficiency.

Gabor DrescherView profile

Researcher

• Operating Systems
• Parallel Computing
• Real-Time Systems
• +4 more

Dr. Gabor Drescher is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He holds a Dr.-Ing. (PhD) in Computer Science from FAU, where he has been a member of the research staff since 2012. His work focuses on operating systems for many-core architectures, real-time systems, parallel computing, and security mechanisms in distributed environments. Education: 2007–2010: Bachelor of Science in Computer Science, FAU 2010–2012: Master of Science with Honors in Computer Science, FAU 2021: Dr.-Ing. in Computer Science (PhD), FAU Research interests include: LAOS (Latency-Aware Operating Systems) Non-blocking synchronization algorithms Invasive Runtime Support Systems (iRTSS) Kernel-level security and encryption (e.g., RamCrypt) Custom OS design for many-core processors Teaching includes courses on operating systems, concurrent systems, and configurable system software engineering. He has supervised multiple student theses on topics like NUMA-aware memory distribution and kernel-level security mechanisms. His research contributes to improving the scalability and predictability of operating systems in many-core environments, with applications in embedded real-time systems and high-performance computing.

• Computer Networking
• Multimedia Systems
• Distributed Systems
• +3 more

Toerless Eckert is a former academic staff member affiliated with Friedrich-Alexander-Universität Erlangen-Nürnberg's Department of Computer Science 4 (Distributed Systems and Operating Systems). He contributed to projects at the Computing Center (RRZE) and IMMD-IV, focusing on network infrastructure and multimedia systems. His work included supporting the Bavarian Higher-Education Network (BHN) and developing tools like DATlib, DoubleX, and vic.FAU. Eckert's research emphasized video conferencing technologies (e.g., VIC extensions for Parallax/Osprey cards) and network protocols. He supervised student projects such as Marcus Meissner's Diplomarbeit on vic.FAU and Stefan Behlert's work on video codecs. His 2018 presentation at CNSM introduced Preferred Path Routing (PPR) for advanced network management. Though his homepage is outdated, his contributions to distributed systems and multimedia networking remain significant.

Stefan PossannerView profile

Research Fellow

• Plasma Physics
• Numerical Analysis
• Computational Methods
• +3 more

Dr. Stefan Possanner is a Research Fellow at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Mathematics. Since 2021, he has served as a research associate at the Max Planck Institute for Plasma Physics within the Numerical Methods in Plasma Physics (NMPP) department led by Prof. Dr. Eric Sonnendrücker. His work bridges theoretical plasma physics with advanced computational techniques, contributing significantly to fusion research initiatives. Dr. Possanner's research expertise spans multiple critical domains in computational plasma physics: Numerical methods for kinetic equations in six-dimensional phase space Computational magnetohydrodynamics for tokamak and stellarator configurations Geometric numerical integration preserving physical system properties Structure-preserving finite element methods for plasma applications High-performance computing implementations for plasma simulations As an active educator since 2014, he has taught numerous courses at TUM including Computational Plasma Physics, Geometric Methods for Physics of Magnetized Plasmas, and advanced mathematics for engineering disciplines. His teaching portfolio demonstrates consistent expertise across computational mathematics and its physics applications. Dr. Possanner contributes to TUM's collaborative research ecosystem with the Max Planck Institute, focusing on: Development of numerical methods for the Vlasov equation and gyrokinetic approximation Modeling approaches for magnetohydrodynamics Geometric numerical integration for reduced complexity models Advanced finite element techniques with enhanced stability properties