جست‌وجوی استاد و پژوهشگر

Christian Schifferمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Medical Imaging
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Christian Schiffer is a Research Fellow at the Research Centre Jülich, leading the 'Large-scale AI for Brain Mapping' team within the Institute of Neuroscience and Medicine (INM-1). His work focuses on developing deep learning algorithms for automated analysis of cytoarchitectonic brain structures using high-resolution histological data. He heads the Helmholtz AI Young Investigator Group, which integrates contrastive learning, graph neural networks, and high-performance computing (HPC) workflows to advance brain mapping technologies. His research applies methods like convolutional neural networks (CNNs) and generative models to extract microstructural features from petabyte-scale microscopy datasets. Key contributions include 3D cytoarchitectonic mapping, integrating topology with histological data, and improving computational efficiency for large-scale neuroimaging analysis. Schiffer also collaborates with the 'Big Data Analytics' group, advancing tools for 3D reconstruction and interactive AI applications in neuroscience. Notably, he received the Helmholtz AI Award 2023 for his contributions to AI-driven neuroscience. His work bridges artificial intelligence, supercomputing, and neuroanatomy to create data-driven brain atlases, enabling deeper insights into brain connectivity and function. Schiffer’s group actively contributes to open-source tools like the Julich-Brain platform and participates in initiatives such as the 'BigBrain' project.

Stefan Heimمشاهده پروفایل

استاد

• Neuroanatomy
• Language Processing
• Cognitive Neuroscience
• +۱ مورد دیگر

Prof. Dr. Stefan Heim leads the Neuroanatomy of Language working group at Research Center Jülich's Institute of Neuroscience and Medicine (INM-1). His research bridges cognitive neuroscience and computational approaches to language processing. Research Focus His work investigates the structural and functional organization of language networks in the brain, combining neuroanatomical approaches with advanced computational methods. Current projects explore machine learning applications in neuroscience and computational linguistics. Publication Trends Recent publications focus on machine learning innovations including large language models, efficient training techniques, and applications in scientific domains like plasma physics and renewable energy.

Markus Heglandمشاهده پروفایل

استاد

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +۴ مورد دیگر

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

• Laser Plasma Physics
• High-Performance Computing (HPC)
• Computational Physics
• +۳ مورد دیگر

René Widera is a researcher at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), specifically within the Laser Particle Acceleration department of the Institute of Radiation Physics. His work focuses on advancing high-performance computing (HPC) techniques for plasma simulations, particularly leveraging GPU architectures and exascale computing frameworks. He contributes to the development and optimization of the PIConGPU code, a leading particle-in-cell (PIC) simulation tool. His research integrates machine learning for real-time data analysis, parallel algorithms for HPC scalability, and cross-platform visualization strategies. Areas of expertise include laser plasma acceleration, high-energy-density physics, and the design of efficient numerical methods for large-scale simulations. He explores hardware-agnostic solutions for computational challenges, including memory access optimizations and DAG-based parallelism. Collaborations involve international HPC initiatives and open-source software projects like openPMD and alpaka . Key projects include the TWEAC initiative to overcome limitations in laser-wakefield acceleration and the development of in-situ visualization pipelines for real-time simulation insights. He also evaluates modern GPU architectures (e.g., AMD, ARM-based systems) for scientific workloads. His contributions bridge theoretical plasma physics with practical computational advancements, aiming to enable next-generation high-intensity laser experiments.

• Scientific Computing
• Data Management
• Research Software Development
• +۳ مورد دیگر

Dr. Bernadette Fritzsch is a researcher at the Alfred Wegener Institute (AWI), specializing in scientific computing, data management, and research software engineering. She leads initiatives in High-Performance Computing (HPC) resource utilization and manages the Meereisportal.de sea ice data platform. Her work emphasizes sustainable research software practices and interdisciplinary collaboration in climate and polar research. Key Responsibilities: Research software development and lifecycle management Leadership in HPC resource coordination (DKRZ) Maintenance of the meereisportal.de information platform Advocacy for open science and FAIR data principles Research Interests: Focuses on advancing research software engineering (RSE), improving data management frameworks, and creating scalable solutions for climate and polar data analysis. Active in policy development for sustainable software practices through Helmholtz initiatives and national/international collaborations. Recent Contributions: Her work includes defining best practices for research software (2019), advancing collaborative climate data grids (C3Grid), and developing workflows for distributed environmental data processing. Publications span software sustainability, interdisciplinary computing, and sea ice data platform development. Grants & Projects: Core contributor to Helmholtz Digitalization Strategy initiatives Member of German Reproducibility Network (GRN) Lead in EU-funded projects like REKLIM and MarESys Labs & Teams: Part of the AWI Computing and Data Centre team, collaborating with Helmholtz Centres and international partners on infrastructure for climate modeling and data analysis.

Philipp Neumannمشاهده پروفایل

استاد

• High Performance Computing
• Molecular Dynamics Simulations
• Multiscale Simulations in Fluid Dynamics
• +۵ مورد دیگر

Philipp Neumann is a Professor of Informatics with a focus on High Performance Computing and Data Science, holding a joint appointment at DESY and the University of Hamburg. As a Leading Scientist and Head of IT at DESY, he coordinates the Helmholtz Federated IT Services (HIFIS) and specializes in HPC for molecular and multiscale simulations. Diploma in Engineering Mathematics (2008), Friedrich-Alexander-University Erlangen-Nuremberg Doctorate in Scientific Computing (2013), Technical University of Munich Habilitation in Scientific Computing (2019), University of Hamburg Neumann's research spans high-performance computing, molecular dynamics, machine learning, and exascale applications. He develops tools like MaMiCo and AutoPas for multiscale and particle simulations, with applications in climate modeling, proteomics, and industrial use cases. His work emphasizes energy efficiency, fault tolerance, and automated algorithm selection. Recent publications highlight advancements in HPC software (e.g., xbat ), data harmonization (e.g., HarmonizR ), and simulation methodologies (e.g., aerodynamic lens systems, multiscale fluid dynamics). These reflect his expertise in bridging molecular and continuum-scale simulations with data-driven techniques. At DESY, Neumann leads IT infrastructure initiatives, including federated IT services for the Helmholtz Association. He contributes to projects like hpc.bw and serves on committees for international HPC conferences, demonstrating leadership in software development and computational science education.

Vincent Heuvelineمشاهده پروفایل

استاد

• Uncertainty Quantification
• High Performance Computing
• Data Intensive Computing
• +۳ مورد دیگر

Professor Vincent Heuveline serves as Professor and Head of the Engineering Mathematics and Computing Lab (EMCL) at Heidelberg University's Interdisciplinary Center for Scientific Computing (IWR). He concurrently holds the positions of Chief Information Officer (CIO) and Director of the Computing Centre at the university, while leading the Data Mining and Uncertainty Quantification (DMQ) group at HITS gGmbH. His academic background includes: Studies in Mathematics, Physics, and Computer Science at Caen (France) and Würzburg (Germany) PhD in Computer Science (1997) from Université de Rennes and INRIA Habilitation in Mathematics (2002) at Heidelberg University Research focuses on uncertainty quantification (UQ) , high-performance computing (HPC) , and data-intensive computing with applications in medical engineering. He is deeply engaged in IT security research and teaching, developing practical frameworks for industrial deployment of numerical simulations and secure computing infrastructures. Recent publications reveal converging trends in biomedical image segmentation (e.g., Biomedisa platform), metabolic modeling for newborn diagnostics, and cybersecurity solutions like DGA detection. Key interdisciplinary themes include machine learning for medical applications, HPC algorithm optimization, and network security frameworks addressing real-world industrial challenges. He leads the EMCL and DMQ research groups, driving innovation in computational science. As CIO, he shapes Heidelberg University's digital infrastructure strategy while maintaining active industry collaborations for translating academic research into simulation and security solutions.

Long Chengمشاهده پروفایل

پژوهشگر

• Cloud Computing
• Big Data Analytics
• Parallel and Distributed Systems
• +۳ مورد دیگر

Dr. Long Cheng was a former Researcher at the International Center for Computational Logic (ICCL) within the Faculty of Computer Science at TU Dresden. His research focused on cloud computing, big data analytics, and distributed systems, particularly in optimizing large-scale data processing, outer join operations, and semantic web technologies. He contributed to projects involving parallel programming models like X10, MapReduce frameworks, and efficient data compression techniques for RDF datasets. Cheng collaborated with the Knowledge-Based Systems research group, addressing challenges in scalable systems, high-throughput indexing, and parallel reasoning under non-monotonic logics. His work emphasized performance optimization in distributed environments, including skew handling, data redistribution strategies, and algorithm scalability for large datasets. Publications span topics such as outer join evaluation in cloud environments, efficient compression of semantic web data, and high-performance query execution over distributed systems. His research bridges theoretical computational logic with practical applications in big data and cloud infrastructure.

Maria Elena Innocentiمشاهده پروفایل

استاد

• Plasma Physics
• High Performance Computing
• Space Physics
• +۳ مورد دیگر

Prof. Dr. Maria Elena Innocenti is a Professor of Computational Plasma Physics at Ruhr-Universität Bochum, Germany. Her research focuses on multiscale processes in heliospheric plasmas, including magnetic reconnection, turbulence, and instabilities. She specializes in High Performance Computing (HPC) methods and applies machine learning to analyze space plasma data. She leads the AIDA (Artificial Intelligence Data Analysis) work package under the Horizon 2020 project and collaborates with NASA’s HERMES science center. Education: PhD in Applied Mathematics (KU Leuven, Belgium), Master of Science and Bachelor of Science in Physics Engineering (Turin Polytechnic, Italy, both with distinction). Professional Highlights: Developed the multi-level multi-domain method and kinetic Expanding Box Model for plasma simulations. Holds fellowships from NASA and FWO (Research Foundation - Flanders). Extensive postdoctoral experience at KU Leuven and NASA’s Jet Propulsion Laboratory. Research Interests: Combining kinetic plasma modeling, HPC optimization, and data-driven approaches to study solar wind dynamics and magnetic energy storage in space plasmas.

Thomas Zeiserمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Lattice Boltzmann Methods
• Computational Fluid Dynamics
• +۳ مورد دیگر

Dr. Thomas Zeiser serves as Head of Systems & Services and Chief Operating Officer HPC at NHR@FAU (Center for National High Performance Computing Erlangen) at Friedrich Alexander University Erlangen-Nuremberg. He leads the Systems & Services group of NHR@FAU and HPC4FAU since the end of 2020 and serves as deputy for NHR@FAU in the NHR Betreiberausschuss. His work focuses on transitioning from serving FAU only to national center operations, managing HPC systems, procuring new infrastructure, financial controlling of NHR@FAU's budgets, and supporting planning for a new data center building. Dr. Zeiser's research interests center around High-Performance Computing with specific expertise in Lattice Boltzmann Methods , large-scale simulations, evaluation of HPC hardware and software, and efficient operation of HPC systems. His work bridges the gap between theoretical computational methods and practical implementation in high-performance environments. He has implemented the first job-based job and performance monitoring for RRZE's HPC systems and has extensive experience in procurement of HPC infrastructure. Analysis of Dr. Zeiser's publication record reveals a consistent focus on practical applications of computational methods in HPC environments. His research demonstrates a progression from fundamental lattice Boltzmann method development toward increasingly complex system-level concerns including fault tolerance, performance monitoring, energy efficiency, and scalable implementations. The publications show strong collaboration patterns with researchers at FAU and other German institutions, particularly in the areas of computational fluid dynamics and parallel computing. Dr. Zeiser regularly serves as a reviewer for various journals and compute time commissions of different HPC centers. He is also one of the local organizers for the Ferienakademie of TUM, FAU, and Universität Stuttgart held in Sarntal. His work with NHR@FAU positions him at the forefront of national high-performance computing infrastructure in Germany. At NHR@FAU and RRZE (Regional Computing Center Erlangen), Dr. Zeiser leads teams responsible for operating some of Germany's most powerful academic supercomputing resources. His group plays a critical role in supporting computational research across multiple disciplines at FAU and increasingly at the national level through the NHR initiative.

Hans-Joachim Bungartzمشاهده پروفایل

استاد

• Scientific Computing
• Fluid Mechanics
• Plasma Physics
• +۴ مورد دیگر

Prof. Hans-Joachim Bungartz is a Full Professor of Scientific Computing at the Technical University of Munich (TUM), leading the Department of Computer Science. He holds the TUM School of Computation, Information and Technology affiliation. His career includes roles at the University of Augsburg and Stuttgart, and he has been at TUM since 2004. He specializes in scientific computing, focusing on numerical algorithms, HPC software, and applications in fluid mechanics, plasma physics, and quantum simulations. Education: Bachelor/Master in Mathematics, Informatics, and Economics (TUM, 1982–1989) PhD (1992) and Habilitation (1998) in Sparse Grids and Numerical Methods (TUM) Research Interests: His work spans adaptive grids, parallel computing frameworks (e.g., Peano), and interdisciplinary applications in computational engineering. He emphasizes bridging modeling, algorithms, and HPC infrastructure. Awards: ISC PRACE Award (2013) Bavarian Habilitation Award (1994) His contributions include over 150 publications and leadership roles in institutions like the Leibniz Supercomputing Center and the TUM Graduate School. Leadership: As Dean of the Informatics Department (since 2013) and Director of the TUM Graduate School, he shapes academic policies. He chairs key national/international bodies like the German Research Network (DFN) Executive Board (2011–2020).

Mathias J. Krauseمشاهده پروفایل

مدرس ارشد

• Applied Mathematics
• High Performance Computing
• Computational Fluid Dynamics
• +۱ مورد دیگر

Dr. Mathias J. Krause is a Senior Lecturer in the Department of Mathematics at Karlsruhe Institute of Technology (KIT). He leads the interdisciplinary Lattice Boltzmann Research Group (LBRG) since April 2013 and has habilitated in 2021. His work spans applied mathematics, computational fluid dynamics (CFD), and high-performance computing (HPC), with a focus on optimization under partial differential equation constraints. Education: Studied mathematics with economics at the University of Karlsruhe (TH) and Cardiff University, graduating in 2006. Research: Specializes in numerical simulations using Lattice Boltzmann Methods (LBM) for fluid flows, with applications in engineering and interdisciplinary projects. Awards: Recognized at the Itanium Solutions Alliance Innovation Awards (2007, 2009) and Mimics Innovation Award (2011). Leadership: Initiator of the open-source library OpenLB, coordinator for international exchange programs, and key contributor to tutor training and PR initiatives at KIT.

Felipe Gonzalez Cornejoمشاهده پروفایل

پژوهشگر

• Computational Fluid Dynamics
• Numerical Methods
• Additive Manufacturing
• +۴ مورد دیگر

Felipe Gonzalez Cornejo is a Researcher at the Chair for Computational Analysis of Technical Systems (CATS) at RWTH Aachen University, affiliated since 2019 (PhD student until 2023, postdoc thereafter) and a member of NHR4CES's Simulation and Data Lab Fluids since 2023. Education: Bachelor's and Master's in Mechanical Engineering, University of Santiago of Chile (Usach) His research develops numerical methods for extrusion-based additive manufacturing, focusing on Computational Fluid Dynamics (CFD) for free-surface flows and non-Newtonian fluids, stabilized finite-element formulations, adaptive mesh techniques, and moving-domain simulations. He integrates High-Performance Computing (HPC) to model thermofluid flow, phase change, and thermoplastic behavior in Fused Deposition Modeling (FDM). No scientific awards were mentioned. He offers thesis supervision on FDM simulation topics: Multi-filament deposition and porosity estimation Nozzle geometry optimization via model order reduction Mesh-update techniques for moving multi-domain simulations HPC tools including load rebalancing and adaptive mesh refinement He collaborates across CATS work groups: production engineering, fluid-structure interaction, and INTERESST.

Manuel Keßlerمشاهده پروفایل

استاد مدعو

• Helicopter Aeromechanics
• Aeroacoustics
• High-Fidelity CFD Simulation
• +۳ مورد دیگر

Manuel Keßler is an apl. Prof. Dr. and Adjunct Professor at the University of Stuttgart's Institute of Aerodynamics and Gas Dynamics , serving as Deputy Head of the Institute and Head of the Helicopters and Aeroacoustics working group. His research focuses on Helicopter Aeromechanics , Aeroacoustics , and High-Fidelity CFD Simulations . He leads projects involving rotorcraft noise reduction, computational fluid dynamics validation, and advanced simulation frameworks like the FLOWer solver. His work integrates experimental and numerical methods, with applications in compound helicopters (e.g., Airbus Helicopters' RACER), eVTOL systems (Volocopter), and distributed propulsion demonstrators. Keßler's technical expertise includes programming in C++, Python, and various assemblers, alongside experience with microcontroller systems (AVR, ARM7, etc.). He oversees the Institute's HPC-cluster (Prandtl) and teaches courses on Fluid Mechanics, Aeroacoustics, and programming optimization. His research spans rotor-fuselage interactions, dynamic stall mitigation, and aeroelastic phenomena, with a strong emphasis on validating numerical models against flight-test data. Notable contributions include numerical investigations of noise sources in distributed propulsion systems, CFD simulations of rotorcraft in ground effect, and aeroacoustic studies of counter-rotating open rotors. His interdisciplinary approach bridges computational methods with real-world engineering challenges, advancing rotorcraft design and noise mitigation strategies.

Jan Liedmannمشاهده پروفایل

استاد

• Structural Mechanics
• Finite Element Analysis
• Environmental Mechanics
• +۷ مورد دیگر

Jan Liedmann is a Professor at the Institute for Statics and Dynamics of Aerospace Structures (ISD) within the Faculty 6: Aerospace Engineering and Geodesy at the University of Stuttgart. His research focuses on structural mechanics, dynamics, environmental mechanics, and biomechanics with methodological emphasis on finite element analysis, machine learning integration, and high-performance computing (HPC). He leads projects involving uncertainty quantification, porous media simulation, and methane oxidation studies. Affiliations: DFG Cluster of Excellence SimTech, SPP 1886, SPP 2311, and FOR 5151 HPC Resources: Vulcan cluster, Hawk supercomputer, bwUniCluster 2.0 Key research areas include experimental mechanics, carbon fiber composite durability, and sea ice modeling. Recent work emphasizes physics-informed machine learning and adaptive finite element methods. The ISD operates advanced labs for static/dynamic fatigue testing and biomaterial analysis, supported by institutional GPU servers. His team collaborates with the Alfred Wegener Institute (AWI) on Antarctic projects and participates in EU initiatives like DigiTain for sustainable aerospace design. Recent publications highlight innovations in separable DeepONet architectures and multiphysics modeling frameworks.