Find Academic Supervisors

• Nuclear Safety Research
• Hydrogen Safety
• Catalytic Hydrogen Recombiners
• +5 more

Dr. Ernst-Arndt Reinecke is Head of the Safety Research department at Forschungszentrum Jülich's Institute of Energy Technology (IEK-6). Holding a PhD from RWTH Aachen University (1999), he has dedicated his career to nuclear safety research with a specialized focus on hydrogen safety and containment phenomena during severe accidents in light water reactors. His leadership extends to international collaborations including founding membership in the International Association HySafe (2009) and participation in the European Hydrogen Safety Panel since 2018. Dr. Reinecke's research interests center on catalytic hydrogen recombiners, severe accident analysis, and hydrogen mitigation strategies in nuclear containment systems. His work bridges fundamental research with practical safety applications, particularly in developing passive safety systems that prevent hydrogen explosions during nuclear accidents. His expertise spans both experimental validation and computational modeling of hydrogen behavior under accident conditions. His extensive publication record reveals consistent focus on improving nuclear safety through better understanding of hydrogen behavior, development of passive autocatalytic recombiners (PARs), and validation of safety codes. Recent work emphasizes the application of nuclear hydrogen safety knowledge to emerging hydrogen technologies, demonstrating the transferability of nuclear safety expertise to broader energy applications. As department head, Dr. Reinecke leads numerous national and international projects including AMHYCO and SASPAM-SA, focusing on hydrogen safety in nuclear applications and small modular reactors. His team conducts experimental research using facilities like REKO and THAI to validate computational models for hydrogen behavior and mitigation.

Kaveh Haghighi MoodView profile

Researcher

• High Performance Computing
• GPU Computing
• Quantum Monte Carlo
• +3 more

Dr. Kaveh Haghighi Mood is a computational researcher at Forschungszentrum Jülich's Jülich Supercomputing Centre (JSC), specializing in high-performance computing with emphasis on GPU acceleration and scientific application enablement. His work bridges atmospheric science, materials simulation, and exascale computing through the Helmholtz Association research infrastructure. His research focuses on optimizing computational methods for next-generation supercomputers, particularly in three domains: GPU-accelerated atmospheric modeling through the MPTRAC framework for Lagrangian transport simulations Exascale benchmarking via the JUPITER suite for evaluating future supercomputing architectures Quantum Monte Carlo methods applied to electronic structure theory and materials science Recent publications demonstrate expertise in CUDA, OpenACC, and performance portability across diverse hardware platforms. Haghighi Mood's publication trends reveal an evolving focus from foundational quantum chemistry (2010-2019) toward GPU optimization and exascale readiness (2020-2025). His work increasingly addresses atmospheric science applications while maintaining strong connections to materials simulation, reflecting JSC's strategic emphasis on climate modeling and computational materials design. The interdisciplinary nature spans computer architecture, numerical methods, and domain-specific scientific computing.

Lars HoffmannView profile

Researcher

• Computational Science
• High-Performance Computing
• Earth System Modeling
• +2 more

Lars Hoffmann is a leading researcher at the Jülich Supercomputing Centre (JSC) , where he heads the HPC in Applied Sciences and Engineering division and co-leads over 40 researchers across five specialized teams. His work focuses on advancing Earth system science through innovative model development, numerical weather prediction, and atmospheric remote sensing analysis. He develops critical tools like the JURASSIC and MPTRAC models, which support research in climate science, astrophysics, and energy systems engineering. Research Pillars: Earth system modeling, high-performance computing, atmospheric science, remote sensing Key Projects: Joint Lab ExaESM, ICON model integration, stratospheric gravity wave analysis Collaborative Networks: Helmholtz Information Program, European research consortia His research explores atmospheric transport processes, gravity wave dynamics, and their impacts on climate and weather systems. Recent work includes: Analyzing gravity wave interactions with hurricanes and volcanic eruptions Developing GPU-accelerated models for computational efficiency Investigating polar stratospheric cloud formation mechanisms Linking satellite observations with numerical simulations Studying troposphere-to-ionosphere wave propagation Evaluating meteorological reanalysis datasets for climate studies

Stefan KriegView profile

Professor

• Lattice Quantum Chromodynamics
• HPC in physics
• Simulations of strongly correlated systems
• +4 more

Professor Stefan Krieg is a distinguished physicist holding dual appointments as a Professor of Physics at the Helmholtz Institute for Radiation and Nuclear Physics of the University of Bonn and as a senior staff member at the Jülich Supercomputing Centre (JSC) at Forschungszentrum Jülich. He currently serves as Head of Division for HPC for Quantum Systems at JSC and leads the Simulation and Data Laboratory for Numerical Quantum Field Theory within the Center for Advanced Simulation and Analytics (CASA), where he also acts as speaker for the "CASA Assembly". Krieg received his PhD in physics from Wuppertal University and completed postdoctoral positions at Forschungszentrum Jülich, Wuppertal University, and MIT before establishing his current leadership roles. Professor Krieg's research program centers on lattice quantum chromodynamics (Lattice QCD) with particular emphasis on nucleon structure calculations, parton distribution functions, and the study of strongly correlated systems. His work spans both theoretical developments and practical computational implementations, with recent publications demonstrating expertise in applying machine learning techniques to address sign problems in quantum simulations. Krieg has made significant contributions to understanding hadron spectroscopy, particularly in exotic states and charmed mesons, while also advancing computational methodologies for high-performance computing systems. His research bridges theoretical physics with practical computing challenges, focusing on efficient software implementations for the Modular Supercomputing Architecture developed at JSC. Analysis of Professor Krieg's publication record reveals a sustained research trajectory focused on fundamental questions in particle and nuclear physics using lattice methods. His work shows increasing integration of machine learning approaches with traditional lattice techniques, particularly evident in recent papers addressing sign problems in quantum simulations. The research spans multiple subfields including hadron spectroscopy, nucleon structure, parton distribution functions, and computational methods for quantum field theories. Krieg frequently collaborates with international research teams, appearing as co-author on numerous multi-institutional projects that leverage high-performance computing resources for fundamental physics investigations. As Head of Division at JSC, Professor Krieg oversees significant research infrastructure and personnel dedicated to quantum systems simulation. His leadership extends to the Center for Advanced Simulation and Analytics where he coordinates the CASA Assembly. While specific grant details aren't provided in the source material, his extensive publication record and leadership positions indicate substantial research funding and collaborative projects. Krieg's work represents a critical intersection of theoretical physics, computational science, and high-performance computing infrastructure development.

Samuel MaloneyView profile

Researcher

• High Performance Computing
• Computer Architecture
• Computational Methods
• +1 more

Dr. Samuel Maloney is a Researcher at Forschungszentrum Jülich's Jülich Supercomputing Centre (JSC), where he leads the Research Group "Next Generation Architectures and Prototypes". His work focuses on advancing supercomputing infrastructure through innovative hardware and software solutions, contributing to JSC's role as a leading European high-performance computing facility. Maloney's research spans High Performance Computing (HPC), computer architecture, and computational numerical methods. He specializes in optimizing resource utilization via advanced monitoring systems, developing job scheduling algorithms for disaggregated memory architectures, and creating meshfree schemes for anisotropic field simulations. His interdisciplinary approach bridges computer science, computational physics, and materials engineering. Analysis of his 2023-2024 publications reveals a strategic focus on exascale computing challenges, particularly efficient resource management and novel computational techniques for next-generation supercomputers. Key themes include disaggregated memory systems, anisotropic field modeling, and sustainable HPC operations, reflecting current industry priorities in scalability and energy efficiency. The "Next Generation Architectures and Prototypes" group under Maloney's leadership actively prototypes future supercomputing technologies through international collaborations, positioning JSC at the forefront of architectural innovation for upcoming exascale and post-exascale systems.

Rainer NiekampView profile

Researcher

• Computational Mechanics
• Uncertainty Quantification
• Multiphysics Systems
• +6 more

Dr.-Ing. Rainer Niekamp is a Researcher at the Department of Civil Engineering, Faculty of Engineering, University of Duisburg-Essen, where he has worked since 2016. He holds a diploma and doctorate in mathematics with a focus on computational mechanics and stochastic modeling. Studied mathematics and computer science at the University of Hannover (1986–1993) Doctoral degree in Civil Engineering from the University of Hannover (2000) Rainer Niekamp’s research spans computational mechanics, multiscale modeling, and stochastic methods. His work includes polynomial chaos expansion for data-driven modeling, partitioned systems for coupled simulations, and parallel software frameworks for large-scale problems. He specializes in model reduction , nonlinear dynamics , and component-based software engineering . His publications highlight multiphysics problems (e.g., thermo-mechanical coupling in steel processing, offshore wind energy converters) and software architecture for scientific computing. He has supervised numerous theses on topics like PeriDynamics simulations , multi-objective optimization , and heterogeneous network modeling .

Juliane DannbergView profile

Professor

• Computational Geodynamics
• Mantle Plumes
• Oceanic Plate Tectonics
• +4 more

Juliane Dannberg is a W2 Professor at GEOMAR Helmholtz Centre for Ocean Research Kiel and Head of the Computational Geodynamics Group within Research Division 4: Dynamics of the Ocean Floor. She leads research in computational geodynamics and mantle processes. Contact: Room 8D/210, Wischhofstraße 1-3, D-24148 Kiel | Phone: +49 431 600-18 08 | Email: jdannberg@geomar.de. Professional Experience: Since May 2024: Professor at GEOMAR 2019–2024: Assistant Professor at University of Florida 2018–2019: Assistant Project Scientist at UC Davis 2017–2018: Postdoctoral Fellow at Colorado State University 2015–2017: Postdoctoral Research Associate at Texas A&M University Education: PhD in Geophysics, GFZ German Research Centre/University of Potsdam (2012-2016) Diplom (MSc) in Geophysics, Friedrich-Schiller-University Jena (2007-2012) Her research develops computational models of Earth's interior dynamics, focusing on mantle plumes, oceanic plate evolution, mantle convection heterogeneity, and magma transport systems. She integrates approaches from geodynamics, seismology and mineral physics while advancing numerical methods for geodynamic simulations. Awards & Honors: Jason Morgan Early Career Award (AGU, 2021) KlarText Science Communication Prize (2017) Computational Geodynamics Distinguished Speaker (2022/2023) She leads the Computational Geodynamics Group at GEOMAR, conducting interdisciplinary research on deep Earth processes through advanced numerical modeling techniques.

Timo HoubenView profile

Researcher

• Environmental Data Science
• Machine Learning for Environmental Applications
• Air Quality Monitoring and Modelling
• +3 more

Timo Houben is a Geoscientist, Data Scientist, and Modeller working in the Department of Monitoring and Exploration Technologies at the Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany. His work focuses on environmental data science, machine learning applications for environmental monitoring, and groundwater modeling. He is actively involved in several research projects including AIAMO (Artificial Intelligence and Mobility) and has contributed to developing tools for urban air quality monitoring and soil moisture prediction. Houben holds a Ph.D. in Groundwater Modeling from the Helmholtz Centre for Environmental Research - UFZ (2017-2020), supervised by Sabine Attinger. Prior to this, he earned a Master of Science in Applied Geosciences with a focus on Engineering Geology, Hydrogeology and Geophysics from RWTH Aachen University (2013-2016), and a Bachelor of Science in Applied Geosciences from the same institution (2009-2013). Houben's research spans multiple interdisciplinary areas at the intersection of environmental science and data science. His primary interests include urban small-scale air quality monitoring and modeling, AI and high-performance computing applications for environmental problems, and machine learning for automated quality control of environmental sensor data. He has developed expertise in spectral analysis of environmental signals, particularly for groundwater level fluctuations, and has created Python tools for aquifer characterization. His work bridges theoretical modeling with practical applications for environmental monitoring and management. His recent publications demonstrate a strong trend toward integrating machine learning with environmental monitoring systems. Houben has made significant contributions to spectral analysis methods for groundwater parameter estimation, urban air quality monitoring through digital twin technology, and machine learning applications for soil moisture prediction. His work increasingly focuses on explainable AI methods for environmental applications, particularly for quality control of sparse sensor networks. The interdisciplinary nature of his research connects hydrogeology, data science, and urban environmental management. Houben is actively involved in several research teams and projects. He co-chairs the PhD-Team Data Science at UFZ and previously chaired the Machine Learning Café. His current work with the AIAMO project focuses on developing an urban digital twin for environmentally sensitive mobility planning in Leipzig. He has contributed to the development of AquiPy, a Python tool for aquifer characterization, and has been involved in spectral analysis of groundwater level fluctuations to estimate hydrogeological parameters.

Sebastian MüllerView profile

Researcher

• Hydroinformatics
• Geostatistics
• Environmental Modeling
• +3 more

Sebastian Müller is a Researcher at the Department of Computational Hydrosystems (CHS) at the Helmholtz Centre for Environmental Research – UFZ in Leipzig, Germany. He serves as a Model keeper for the mesoscale Hydrologic Model (mHM) and is the founder of the GeoStat-Framework, a comprehensive Python-based toolkit for geostatistical modeling. His research focuses on developing software tools for hydro-scientific applications, with particular expertise in groundwater modeling, geostatistics, and environmental software development. Müller specializes in creating open-source tools that address complex hydrological challenges, including aquifer heterogeneity analysis and pumping test interpretation. His work bridges the gap between theoretical hydrology and practical software implementation, making advanced modeling techniques accessible to researchers and practitioners. Müller's publication record demonstrates a strong focus on developing computational tools that advance hydrological science. His research trends show a consistent emphasis on creating Python-based frameworks that simplify complex geostatistical analyses and hydrological modeling. His tools have become important resources in the hydroinformatics community, facilitating more accurate groundwater characterization and environmental modeling. Active contributor to multiple open-source geostatistical and hydrological software projects Specializes in Python-based scientific computing for environmental applications Focuses on practical implementation of theoretical hydrological concepts As a Model keeper at UFZ, Müller plays a key role in maintaining and advancing critical hydrological modeling infrastructure used for drought monitoring and water resource management in Germany. His work directly supports operational applications like the German Drought Monitor, which provides daily soil moisture information across Germany.

• Organic Solar Cells
• Perovskite Solar Cells
• Computational Modeling
• +3 more

Tim Albes is an Associate Professor at the Technical University of Munich, affiliated with the Associate Professorship of Simulation of Nanosystems for Energy Conversion under Prof. Alessio Gagliardi. His work focuses on computational modeling of organic solar cells, perovskite solar cells, and electrocatalytic systems using advanced methods like Kinetic Monte Carlo, Drift-Diffusion, and Density Functional Theory (DFT). Academic Rank: Associate Professor Department: Simulation of Nanosystems for Energy Conversion Key Research Areas: Organic Solar Cells, Perovskite Solar Cells, Multiscale Modeling, Machine Learning for Materials Science Albes' recent publications highlight his expertise in charge pair separation dynamics, energetic disorder effects, and blend morphology optimization in organic photovoltaics. His work bridges computational methods with practical device simulations, including applications of Machine Learning to multiscale systems. Teaching & Supervision: He supervises Bachelor's, Master's, and diploma theses at TUM, contributing to academic training in computational nanosystems and energy conversion.

• Kinetic Monte Carlo
• Density Functional Theory
• Molecular Dynamics
• +7 more

Manuel Gößwein is a researcher at the Technical University of Munich (TUM) , affiliated with the Associate Professorship Simulation of Nanosystems for Energy Conversion under Prof. Alessio Gagliardi. His work focuses on computational modeling of electrochemical systems and energy conversion devices. Research Interests Teaching Activities Collaborative Projects Research Areas : Gößwein specializes in developing and applying advanced computational methods including Kinetic Monte Carlo , Density Functional Theory (DFT) , and Multiscale Modeling to study energy materials and interfaces. His work addresses challenges in solid-state electrolytes , organic solar cells , and electrocatalysis , with applications in lithium-ion batteries and hybrid energy devices. Teaching Contributions : He supervises Master, Bachelor, and Diploma theses while supporting research and engineering practices at TUM.

• Organic Solar Cells
• Perovskite Solar Cells
• Electrochemical Systems
• +3 more

Waldemar Kaiser is an Associate Professor at the Technical University of Munich (TUM) within the Associate Professorship of Theory of Functional Energy Materials . His research focuses on computational modeling of organic and perovskite solar cells, electrochemical systems, and multiscale simulations. Research Highlights : Charge/exciton transport in organic semiconductors Non-equilibrium thermodynamics in photovoltaic devices Defect dynamics in lead-halide perovskites Machine learning for charge transfer integrals Ion migration in electrochemical systems Publication Trends (2024–2018) show expertise in Kinetic Monte Carlo , Molecular Dynamics , and Machine Learning applied to organic electronics, perovskite solar cells, and magnetic nanomaterials. Collaborations span institutions in Germany, Italy, and France. Active Projects : DFG e-Conversion Cluster III (Solid-Solid Interfaces) TUM Innovation Network ARTEMIS EU Lion-Hearted Teaching includes supervision of Master/Bachelor theses at TUM and participation in computational photonics research.

• Extreme Weather Events
• Heavy Precipitation and Floods
• Statistical Climatology
• +3 more

Florian Ehmele is a Researcher at the Institute of Meteorology and Climate Research Troposphärenforschung (IMKTRO) at the Karlsruhe Institute of Technology (KIT), Germany. He has held research positions at KIT since 2013, including his current role as Research Associate since 2025, following project management work at UBIMET GmbH (2023-2025). Education: Diploma in Meteorology, Karlsruhe Institute of Technology (2007-2013) PhD in Meteorology, Karlsruhe Institute of Technology (2014-2017) Ehmele's research centers on extreme weather events, particularly heavy precipitation and floods, using statistical methods and stochastic simulations to analyze long-term variations and climate change impacts. His work integrates climate modeling with renewable energy systems and hydrological risk assessment, emphasizing practical applications for disaster management and climate adaptation strategies. His publication record shows consistent focus on Central European flood events (2021), Western Mediterranean extreme precipitation (2018), and Iberian drought patterns. He leverages large regional climate model ensembles to quantify hydrological extremes, with increasing emphasis on multi-disciplinary impact assessments and future climate projections. Ehmele contributes to major initiatives including RegIKlim (current), SCENIC, and FloRis flood risk projects. He has taught graduate-level data analysis and undergraduate meteorological practicums at KIT since 2012, demonstrating commitment to academic training alongside research.

Martin VechevView profile

Professor

• Programming Languages
• Software Analysis
• Static Analysis
• +4 more

Martin Vechev is a Professor in the Department of Computer Science at ETH Zurich, leading the Secure, Reliable, and Intelligent Systems (SRL) Lab. His research bridges programming languages, software analysis, and emerging domains like quantum computing, with significant contributions to static analysis, abstract interpretation, and machine learning for code. His primary research interests include programming languages, static analysis, abstract interpretation, and quantum computing. Vechev has pioneered scalable techniques for software verification, particularly in concurrency and security analysis, and has recently driven innovations at the intersection of programming languages and quantum software development. His work emphasizes practical applications while maintaining theoretical rigor. Analysis of Vechev's recent publications reveals a strategic evolution toward quantum programming languages and machine learning integration. His lab has shifted from traditional static analysis to developing foundational frameworks for quantum circuit synthesis (e.g., Silq, Unqomp) and robust neural network certification, addressing critical challenges in quantum resource management and AI safety. The SRL Lab under Vechev's direction focuses on building intelligent systems that are inherently secure and reliable through advanced program analysis techniques. The lab's research spans theoretical foundations to industrial-strength tools, with strong emphasis on quantum software engineering and the application of machine learning to code understanding and generation.

Michael KrausView profile

Researcher

• Numerical Methods for Fluids and Plasmas
• Structure Preserving Discretisation Methods
• Reduced Complexity Modelling
• +5 more

Dr. Michael Kraus is a Research Group Leader at the Max Planck Institute for Plasma Physics in Garching, Germany, where he leads the "Geometric Numerical Integration and Reduced Complexity Modelling" group. His work focuses on developing structure-preserving discretization methods for problems in fluid dynamics and plasma physics, with recent applications in model order reduction and scientific machine learning. Dr. Kraus received his Doctor of Science (summa cum laude) from Technische Universität München in 2013, with doctoral studies in Numerical Methods in Plasma Physics. He completed a Diploma in Physics from TUM (2010) with major in Physics and minor in Scientific Computing, and earlier studied Physics at Friedrich-Schiller-Universität Jena. His research interests span Numerical Methods for Fluids and Plasmas, Structure Preserving Discretisation Methods, Reduced Complexity Modelling, Scientific Machine Learning, Geometric Mechanics and Field Theory, Symmetries and Conservation Laws, Research Software Engineering, and Scientific Computing with Julia. His work leverages geometric properties of equations to derive accurate and robust numerical schemes that better represent underlying physics while exhibiting good stability properties. Dr. Kraus's recent publications demonstrate a strong focus on geometric numerical integration techniques applied to plasma physics and fluid dynamics. His work consistently emphasizes structure-preserving methods, with particular attention to variational integrators, metriplectic systems, and applications to magnetohydrodynamics, kinetic theory, and the Vlasov-Maxwell system. His research bridges mathematical theory with practical computational implementations. Dr. Kraus has received several prestigious awards including: ANR-DFG Research Grant (2021) Research Grant from International Graduate School of Science and Engineering, TUM (2018) Marie Skłodowska-Curie Individual Fellowship (2016) Otto Hahn Medal of the Max Planck Society (2014) PhD awarded with highest distinction (summa cum laude) (2013) Dr. Kraus actively mentors graduate students, currently supervising multiple PhD candidates in Mathematics at Technische Universität München. His research is supported by significant grants including the ANR/DFG International Project "MILK: MachIne Learning for reduced Kinetic models" (since 2021) and the IGSSE International Project "GEMKIN: Geometric Methods for Kinetic Equations" (2018-2021). As lead author of various Julia packages for Geometric Numerical Integration and Reduced Complexity Modelling, Dr. Kraus maintains a strong focus on research software engineering and scientific computing. His group develops computational tools that implement the theoretical numerical methods he researches, creating a tight feedback loop between theory and practice.