جست‌وجوی استاد و پژوهشگر

Heiner Giefersمشاهده پروفایل

استاد

• Cloud Computing
• Hardware Acceleration
• Field-Programmable Gate Arrays
• +۷ مورد دیگر

Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.

• High-Performance Computing (HPC)
• Computational Fluid Dynamics
• Quantum Computing
• +۳ مورد دیگر

Stefano Markidis is a leading researcher in High-Performance Computing (HPC) and quantum computing. His work focuses on developing advanced simulation frameworks, such as the Neko framework for computational fluid dynamics, and optimizing algorithms for heterogeneous architectures. He collaborates extensively with institutions and researchers globally, contributing to fields like plasma physics, quantum systems, and machine learning applications. His research emphasizes scalability, performance optimization, and the integration of cutting-edge technologies like GPU acceleration and quantum computing. Key research interests include extreme-scale simulations, quantum algorithms, and in-situ data analysis techniques. He has published over 200 articles, with recent work addressing challenges in NISQ systems, tensor network simulations, and CUDA-based performance enhancements. His contributions span theoretical and applied domains, bridging computational methods with real-world applications in fusion energy, materials science, and space exploration. Notable collaborations include projects with Philipp Schlatter, Niclas Jansson, and the NISQ application development community. Markidis also explores hybrid frameworks combining classical and quantum computing, aiming to leverage emerging hardware for scientific breakthroughs.

Felix Fritzenمشاهده پروفایل

استاد

• Data Analytics in Engineering
• Computational Mechanics
• Material Modeling
• +۷ مورد دیگر

Prof. Felix Fritzen is a Heisenberg Professor (W3) for Data Analytics in Engineering at the University of Stuttgart's Institute of Applied Mechanics (MIB). His work is embedded in the Cluster of Excellence Data-Integrated Simulation Science (SimTech). He leads the EMMA Emmy Noether group (2015–2020) and previously headed the KIT Young Investigator Group CAMM. His research focuses on data-driven surrogate models, uncertainty quantification, and computational mechanics of materials, with emphasis on nonlinear model reduction and multiscale simulations. Education: Ph.D. (Dr.-Ing., summa cum laude) from KIT (2011), Dipl.-Math. techn. (2007), Dipl.-Ing. (2006). Research interests include machine learning integration with mechanics, microstructure-property relations, and high-performance simulation techniques. Notable contributions involve FFT-based homogenization, reduced order modeling, and GPU-accelerated methods. He has authored over 60 peer-reviewed papers, including works on surrogate models for microstructure forecasting and thermoelastic material analysis. Awards include the KIT PhD Award (2012) and recognition as a GAMM Junior (2012–2014). His teaching includes courses on data processing for engineers and model order reduction. Funded projects include DFG grants (EXC-2075, HE 7919/1) and the Heisenberg Professorship (FR2702/8).

• Computational Mechanics
• Micromechanics
• Homogenization
• +۳ مورد دیگر

Prof. Dr. Matti Schneider serves as Professor of Engineering Mathematics and Head of the Institute of Engineering Mathematics within the Faculty of Civil Engineering at the University of Duisburg-Essen. His academic leadership spans computational mechanics research and teaching core mathematics courses for civil engineering students. His educational background includes: Diploma in Applied Mathematics with distinction from TU Bergakademie Freiberg (2009) PhD (Dr. rer. nat.) from Leipzig University (2013) on "The Leray-Serre spectral sequence in Morse homology on Hilbert manifolds and in Floer homology on cotangent bundles" Professor Schneider's research focuses on advancing computational methods for solid mechanics through FFT-based homogenization techniques, microstructure modeling, and multi-scale material analysis. His work bridges applied mathematics and engineering to solve complex problems in heterogeneous material systems, with particular emphasis on numerical stability, boundary condition implementation, and efficient solver development for industrial applications. His methodologies enable accurate prediction of material behavior across scales from microscopic structures to macroscopic components. Analysis of his 15 most recent publications reveals dominant trends in FFT-based computational homogenization, with significant contributions to thermal problems, porous media, and fiber-reinforced composites. He pioneers the integration of machine learning (particularly deep material networks) with traditional numerical methods to model complex material behaviors like shear-thinning suspensions and 3D-printed materials. His work consistently addresses computational challenges in boundary condition implementation and convergence for stochastic microstructures. Professor Schneider leads the Institute of Engineering Mathematics and directs research within the ERC-funded BeyondRVE project, which focuses on extending representative volume element concepts for advanced material modeling. His collaborative network includes major German research institutions like Fraunhofer ITWM and international partners in materials science.

• Wireless Communications
• Radio Channel Modeling
• Millimeter Wave Propagation
• +۶ مورد دیگر

Jun-ichi Takada is a prominent researcher in wireless communications with extensive contributions to radio channel modeling, millimeter wave propagation, and wireless body area networks. His work spans over 15 years with consistent high-impact publications in IEEE journals and conferences. Dr. Takada's research focuses on several key areas in wireless communications: Advanced radio channel modeling for 5G/6G systems Millimeter wave propagation characteristics in various environments Wireless body area network (WBAN) channel characterization Channel sounding and measurement techniques Indoor and outdoor propagation modeling Antenna design and performance evaluation Wireless localization and spectrum sharing techniques Analysis of Dr. Takada's recent publications (2022-2025) reveals a strong focus on next-generation wireless systems, with particular emphasis on site-specific channel modeling for 5G/6G, millimeter wave applications, and innovative measurement techniques. His work increasingly integrates machine learning approaches with traditional signal processing methods for wireless channel analysis and human detection applications. Dr. Takada has collaborated extensively with researchers across Japan and internationally, demonstrating leadership in several major research projects related to wireless communications standardization and development.

Lars Pastewkaمشاهده پروفایل

استاد

• Multiscale simulation of materials
• Micromechanics of materials
• Material interfaces
• +۷ مورد دیگر

Prof. Lars Pastewka is a Full Professor of Simulation at the Department of Microsystems Engineering (IMTEK) within the Faculty of Engineering at the Albert Ludwigs University of Freiburg. His research focuses on multiscale simulation of materials, micromechanics, and tribology, with a particular emphasis on friction, adhesion, and wear. He leads a research group developing computational methods to model materials from atomic to continuum scales. His expertise includes surface forces, material interfaces, and the design of programmable materials. Key projects include studies on self-repair mechanisms in biological and artificial materials, tribogenerators, and resilient microfluidic networks. He supervises doctoral researchers such as Indre Jödicke and Mahdi Roshani, with past student Dr. Antoine Sanner completing his doctorate under his guidance. Prof. Pastewka has contributed to open science through tools like dtool and dserver, promoting FAIR data principles. His work spans interdisciplinary collaborations in materials science, biomechanics, and computational engineering. He is actively involved in academic outreach through events like the livMatS Colloquium series and contributes to teaching programs such as the Master Lab and Core Lectures initiatives. His research has been funded by Germany’s Excellence Strategy (EXC-2193/1) and addresses challenges in surface topography analysis, hydrodynamic lubrication, and material fracture mechanics. His group’s computational frameworks, including matscipy and SYMPLER, have advanced large-scale simulations in materials science.

Gerlind Plonkaمشاهده پروفایل

استاد

• Numerical Fourier Analysis
• Wavelet Theory
• Regularization Methods
• +۳ مورد دیگر

Gerlind Plonka is a Professor of Applied Mathematics at the University of Göttingen, specifically within the Institute for Numerical and Applied Mathematics (NAM). Her research focuses on Numerical Fourier Analysis Wavelet Theory Regularization and Nonlinear Diffusion Methods Fast Algorithms and Numerical Stability Signal and Image Processing Applications Her recent publications emphasize structured subsampling in Fourier domains, Prony-type methods for exponential sum recovery, and deep learning integration in medical imaging. She supervises active PhD candidates including Benjamin Kocurov, Anahita Riahi, Yannick Nicola Riebe, and Janina Schmidt, with a legacy of advising over 50 graduates across diverse topics like Sparse FFT Algorithms Phase Retrieval Constraints Wavelet-Based Image Compression Nonlinear Diffusion Filters High-Dimensional Data Approximation

Stephan Marziمشاهده پروفایل

استاد

• Nonlinear fracture mechanics
• Experimental methods of fracture mechanics
• Cohesive zone models
• +۲ مورد دیگر

Prof. Dr.-Ing. Stephan Marzi is a faculty member at the Technical University of Central Hesse (THM) , affiliated with the Department of Mechanical Engineering and Energy Technology . He leads the Fracture and Materials Mechanics working group at the Institute of Mechanics and Materials Research (IMM) and contributes to the Competence Center AutoM. His teaching responsibilities include Fundamentals of Machine Dynamics , Advanced Materials Mechanics , and Higher Dynamics . Research focuses on nonlinear fracture mechanics , cohesive zone modeling , and micromechanical material characterization . Active in international collaborations with institutions like University of Luxembourg , University of Kassel , and Albert Ludwig University of Freiburg im Breisgau . Key funding sources include DFG , BMBF , and BMWI-ZIM for projects on adhesive joint failure , composite material testing , and crash-optimized adhesives . Recent publications highlight advancements in mixed-mode fracture testing , rate-dependent cohesive zone models , and FFT-based homogenization for adhesive joints. He serves as a reviewer and committee member for doctoral theses across multiple universities. Leadership roles: Member of THM's Presidential Committee for Research , Deputy Spokesperson of the IMM , and representative for the ME Department at THM. Research infrastructure: State-of-the-art laboratory equipment and a 400-core computing cluster at IMM.

• Computational Mechanics
• Micromechanics
• FFT-based Methods
• +۴ مورد دیگر

Lennart Risthaus serves as a Researcher at the Department of Engineering Mathematics within the School of Civil Engineering at the University of Duisburg-Essen, Germany. He joined the university in September 2023 after previously working as a Researcher at the Institute of Engineering Mechanics, Continuum Mechanics Division at the Karlsruhe Institute of Technology (KIT) from February 2021 to August 2023. His academic appointments demonstrate a consistent trajectory in computational mechanics research within German technical universities. Dr. Risthaus completed his Bachelor's degree in Mechanical Engineering with a focus on Continuum Mechanics (2014-2018) and Master's degree in Mechanical Engineering with majors in Medical Technology and Applied Mechanics (2018-2021), both from the Karlsruhe Institute of Technology. His educational journey included an Erasmus exchange semester at the Royal Institute of Technology (KTH) in Stockholm and practical experience through internships at Reden B.V. in the Netherlands and Admedes GmbH in Germany, where he worked on finite element simulations and material testing. His research specializes in advanced computational techniques for material science, particularly FFT-based homogenization methods in micromechanics. Risthaus has developed innovative approaches for implementing Dirichlet boundary conditions in FFT-based computational frameworks and pioneered applications of tensor-train formats to enhance computational efficiency. His work bridges theoretical mathematics with practical engineering applications, focusing on solving complex boundary value problems in material behavior analysis. An analysis of his publication record reveals a clear research trajectory toward increasingly sophisticated computational methods for micromechanical simulations. His recent work demonstrates growing expertise in thermal homogenization problems and the integration of tensor-train methods with traditional FFT approaches. The consistent publication in high-impact journals like Computational Mechanics and International Journal for Numerical Methods in Engineering indicates recognition within the computational mechanics community. Risthaus actively contributes to the academic community through presentations at major international conferences including the GAMM Annual Meetings, ECCOMAS Young Investigators Conference, and the International Conference on Computational Plasticity (COMPLAS). His teaching responsibilities include leading exercises and tutorials for Mathematics courses for Civil Engineering students at both undergraduate and graduate levels, demonstrating his commitment to engineering education alongside his research activities.

Dirk Pflügerمشاهده پروفایل

استاد

• Scientific Computing
• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• +۳ مورد دیگر

Dirk Pflüger is a Professor at the University of Stuttgart's Institute of Parallel and Distributed Systems, within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on high-performance computing (HPC), parallel and distributed systems, and sparse grids. He has led projects in astrophysical simulations, machine learning applications, and uncertainty quantification. Notable contributions include developing scalable algorithms for exascale computing using HPX, Kokkos, and SYCL frameworks. His expertise spans distributed computing architectures, task-based parallel programming, and interdisciplinary applications in astrophysics and medical AI. Recent work includes optimizing hyperparameter tuning, simulating stellar mergers, and enhancing blood glucose prediction models using deep reinforcement learning. Pflüger's research emphasizes performance portability, fault tolerance, and cross-platform collaboration. He has contributed to open-source tools like PLSSVM and hws, which address hardware monitoring and GPU acceleration challenges. His work bridges theoretical advancements with practical implementations for real-world computational problems.

• Computational Mechanics
• Material Modeling
• Finite Element Methods
• +۷ مورد دیگر

Prof. Dr.-Ing. habil. Stefanie Reese is a Universitätsprofessorin at the Chair and Institute of Applied Mechanics (Lehrstuhl und Institut für Angewandte Mechanik) at RWTH Aachen University. Her research focuses on computational mechanics, material modeling, and multiscale analysis with applications in polymers, biomechanics, and metamaterials. She leads projects involving advanced numerical methods like FE-FFT-based homogenization, physics-informed machine learning, and constitutive modeling of complex materials. Her work integrates interdisciplinary approaches, combining continuum mechanics with data-driven strategies to address challenges in additive manufacturing, vascular tissue engineering, and smart material systems. Key areas include damage mechanics, viscoelasticity, and thermo-mechanical coupling in polymers and composites. She collaborates on experimental validation, particularly in polymer crystallization kinetics and biohybrid implant development. Recent studies explore in-silico models for in-stent restenosis, predictive frameworks for tissue maturation, and reduced-order modeling techniques for modular structures. Her lab emphasizes bio-inspired design principles and novel material characterization methods, with a focus on real-time simulation tools for engineering applications.

• Computational Mechanics
• Materials Science
• Machine Learning Applications
• +۳ مورد دیگر

Binh Nguyen serves as a Postdoctoral Researcher at the Institute of Engineering Mathematics within the Department of Civil Engineering at the University of Duisburg-Essen since October 2024, specializing in advanced computational methods for material science. His academic foundation includes: PhD in Computational Mechanics from Leibniz University Hannover (2016-2021) Dr. Nguyen's research integrates computational mechanics with artificial intelligence, focusing on FFT-based homogenization techniques, deep material networks, and multi-physics simulations for heterogeneous materials. His current ERC-funded project challenges conventional representative volume element approaches for random heterogeneous materials, pushing boundaries in predictive material modeling through novel numerical frameworks. His scholarly recognition includes: Marie Curie Postdoctoral Fellowship (2023-2024) With extensive international research experience across imec and KU Leuven, Dr. Nguyen has contributed to MEMS development and ERC-funded computational projects, maintaining active collaborations within European materials research networks while advancing his work on beyond-RVE methodologies at Duisburg-Essen. He operates within the Institute of Engineering Mathematics research ecosystem, leveraging high-performance computing infrastructure for large-scale material simulations and methodological innovation in computational homogenization.

• Artificial Intelligence
• Machine Learning
• Space Weather
• +۴ مورد دیگر

Dr. Peter Steinbach leads the Group of Artificial Intelligence at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), focusing on interdisciplinary research spanning computational science, space physics, and machine learning applications. His work integrates advanced computing techniques with domain-specific challenges in healthcare, microscopy, and space weather monitoring. Education & Background: While specific educational details are not provided, his research portfolio indicates advanced expertise in physics, computational methods, and software engineering. His career has been centered at HZDR, contributing to major projects like the CLIJ GPU-accelerated image processing framework and space weather studies using ionospheric wave analysis. Research Interests: Steinbach's work emphasizes practical AI applications, including educational tools for machine learning (e.g., "Teaching Machine Learning in 2020") and medical AI systems. His computational science contributions include optimizing high-performance computing for fluid dynamics and parallel processing. In space physics, he investigates VLF transmitter propagation, plasmaspheric dynamics, and geomagnetic storm impacts using satellite data from DEMETER and ground-based networks. Key Projects: Developed CLIJ, a GPU-accelerated image processing tool integrated with Fiji, revolutionizing microscopy workflows. Advanced understanding of ionospheric processes through studies on Schumann resonances and plasmaspheric hiss effects. Pioneered techniques for lightning detection and Q-burst analysis to map Earth's crust conductivity. Technical Contributions: Steinbach has optimized MPI-based fluid dynamics models for GPU systems and created benchmarking tools like gearshifft for heterogeneous computing platforms. His work bridges theoretical research with practical software solutions for the scientific community.

• Computational Materials Science
• Crystal Plasticity
• Phase-field Modeling
• +۴ مورد دیگر

Dr. Shanthraj Pratheek is a researcher affiliated with multiple institutions including Aachen Institute for Advanced Study in Computational Engineering Science at RWTH Aachen University Microstructure Physics and Alloy Design at Max Planck Society's Max-Planck-Institut für Eisenforschung GmbH The School of Materials at The University of Manchester Henry Royce Institute at The University of Manchester United Kingdom Atomic Energy Authority (UKAEA) His research focuses on multi-physics computational materials science , particularly in areas like crystal plasticity , phase-field modeling , and microstructure evolution . Key topics include chmo-mechanical coupling , damage mechanics , and multi-scale simulation of metallic systems. He has contributed to the development of the DAMASK simulation software for crystal plasticity and multiphysics problems. Recent publications highlight his work on chmoelastic interactions in alloys precipitation kinetics in aluminum systems dislocation segregation mechanisms multi-sublattice phase modeling grain boundary microchemistry FFT-based damage solvers He maintains active collaborations across institutions including University of Manchester, RWTH Aachen, Max Planck Society, and UKAEA, with a focus on computational simulations for materials design and failure analysis.

Matthias Kabelمشاهده پروفایل

پژوهشگر

• Computational Mechanics
• Material Science
• Multiscale Modeling
• +۳ مورد دیگر

Matthias Kabel is a Researcher and Team Manager in the Department of Flow and Material Simulation at the Fraunhofer Institute for Industrial Mathematics ITWM in Kaiserslautern, Germany. His research focuses on computational mechanics and material science, particularly in Multiscale modeling of composite materials FFT-based homogenization techniques Nonlinear elasticity and damage mechanics Efficient voxel-based simulation methods Recent publications highlight his work on FEFFT and phase-field methods for microstructural evolution Composite voxel techniques for nonlinear mechanical problems Progressive damage modeling of composites using FFT solvers These contributions emphasize computational efficiency and accuracy in simulating complex material behaviors. The Department of Flow and Material Simulation at Fraunhofer ITWM develops advanced numerical methods for industrial material science applications, including the FFT-based homogenization framework that underpins Kabel's research.