جست‌وجوی استاد و پژوهشگر

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 Materials Science
• Machine Learning for Materials Predictions
• Metal-Organic Frameworks (MOFs)
• +۷ مورد دیگر

Felix Mayr is a researcher at the Technical University of Munich (TUM) under the Associate Professorship Simulation of Nanosystems for Energy Conversion in the TUM School of Computation, Information and Technology (SoCIT). His work focuses on computational materials science, particularly leveraging machine learning and density functional theory (DFT) for energy-related applications. Institution: Technical University of Munich Department: Simulation of Nanosystems for Energy Conversion Research Interests : Mayr investigates materials for solar cells, electrocatalysis, and gas adsorption using machine learning , DFT , and multiscale modeling . Key projects include optimizing MOF interfaces for PFAS removal and developing algorithms for adsorption site identification. Article Trends : His recent publications emphasize machine learning integration with materials science, targeting MOFs , perovskites , and electrocatalysts . Topics include PFAS remediation, charge prediction, and stability analysis. Projects & Collaborations : Mayr contributes to DFG e-Conversion Clusters (I-III), TUM Innovation Network ARTEMIS, EU Lion-Hearted, and BMWi ptj PrOperPhotoMiLe SOLAR.ERANET.

• Catalysis
• CO₂ Valorization
• Kinetic Modeling
• +۴ مورد دیگر

Dr. Sofia Angeli is a Group Leader at the Institute of Catalysis Research and Technology (IKFT), Karlsruhe Institute of Technology (KIT), Germany, since March 2024. Previously, she held roles as Senior Scientist/Group Leader (2021–2024) and Postdoctoral Researcher (2017–2019) at KIT. Her research focuses on catalysis for energy transition, CO₂ valorization, kinetic modeling, and digitalization of catalytic processes. She leads interdisciplinary projects integrating experimental and computational methods to advance sustainable chemical processes. PhD in Chemical Engineering (2016): Aristotle University of Thessaloniki, Greece. Thesis: Hydrogen production via intensified methane steam reforming processes. M.Sc. in Advanced Materials (2012): Aristotle University of Thessaloniki. Diploma in Chemical Engineering (2009): Aristotle University of Thessaloniki. Research Interests : Sofia’s work spans kinetic modeling, CO₂ conversion, catalytic pollutant removal, and digital tools for catalysis research. She develops novel catalysts for methane reforming and designs data management systems like Adacta and CaRMeN to streamline reaction mechanism analysis. Her projects often address industrial challenges in emissions reduction and renewable energy. Her recent publications emphasize catalytic processes for energy sustainability (e.g., methane oxidative coupling) and automated modeling frameworks. She collaborates widely, contributing to journals like Chemical Engineering Journal and ACS Catalysis . Advising & Teams : As a Group Leader, she oversees researchers in catalysis and data-driven processes. Her team works on cutting-edge projects supported by KIT’s infrastructure and interdisciplinary networks. Labs/Teams : Active in the IKFT and the Deutschmann Group, focusing on catalyst design and process intensification.

Wangzhong Muمشاهده پروفایل

مدرس ارشد

• Sustainable Metallurgy
• Microstructure Physics
• Alloy Design
• +۳ مورد دیگر

Prof. Wangzhong Mu is a Senior Lecturer (Docent) in the Department of Materials Science and Engineering at KTH Royal Institute of Technology, Stockholm. His research focuses on sustainable metallurgy, microstructure physics, and alloy design. He leads the thermo-physical property analysis section in the Hultgren Lab and is affiliated with Digital Futures at KTH. Educations: PhD in Materials Science, KTH Royal Institute of Technology (2015) MSc/Bachelor's in Materials Science, Northeastern University, China Research Interests: Inclusion engineering and microstructure-property correlations in steels High-entropy alloy design using digital tools (AI/thermodynamic modeling) In-situ characterization via confocal microscopy and multiscale analysis Recycling-oriented steel production and CO2 reduction strategies Grants/Projects (selected): SSF Strategic Mobility Grant (2023-2024): Clean steel for sustainable future VINNOVA Mobility Grant (2022-2024): Hydrogen-based metallurgy STINT Project (2022-2023): Inclusion engineering for green steel EIT RawMaterials (ENDUREIT, 2019-2021): Durable steels at intermediate temperatures Labs/Teams: Hultgren Lab (materials characterization), Digital Futures (AI-driven metallurgy), and international collaborations with Hanyang University (South Korea), IIT Bombay (India), and Tohoku University (Japan).

• High Performance Computing
• Performance Engineering
• Performance Modeling
• +۷ مورد دیگر

Gerhard Wellein is a Professor for High Performance Computing at the Department of Computer Science of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He is the head of NHR@FAU (Erlangen National Center for High Performance Computing) and a member of the board of directors of the German NHR-Alliance. Since 2024, he has also served as a Visiting Professor for HPC at the Delft Institute of Applied Mathematics, Delft University of Technology. He holds a PhD in theoretical physics from the University of Bayreuth and has over two decades of experience in HPC education and research. Research Interests: His research focuses on performance modeling and engineering, architecture-specific code optimization, novel parallelization techniques, and the development of hardware-efficient building blocks for sparse linear algebra and stencil solvers. His work bridges computer science, applied mathematics, and computational physics, aiming to maximize efficiency on current and future HPC architectures, including exascale systems. Publication Trends: His recent publications emphasize analytical performance modeling (e.g., Roofline, oscillator models), energy efficiency, GPU optimization, and scalable linear algebra. They reflect a strong focus on both theoretical modeling and practical implementation, with applications in CFD, quantum physics, and molecular dynamics. Scientific Awards: 2011 Informatics Europe Curriculum Best Practices Award (shared with Jan Treibig and Georg Hager) for outstanding teaching contributions in HPC. Grants and Advising: He has led numerous third-party funded projects from the EU, BMBF, and DFG, including EoCoE-III, ESSEX, EXASTEEL, and ProPE. These projects focus on exascale software, performance engineering, fault tolerance, and multiscale simulation. He has mentored multiple researchers and students, contributing to the development of tools such as LIKWID, ClusterCockpit, and GEOPM. Labs and Teams: He leads the HPC research group at FAU and is deeply involved in national and international HPC initiatives. His team collaborates extensively on open-source HPC software and performance tools, fostering a strong community-driven approach to performance engineering.

Julija Zavadlavمشاهده پروفایل

استادیار

• Multiscale Modeling
• Machine Learning
• Computational Chemistry
• +۳ مورد دیگر

Prof. Julija Zavadlav is an Assistant Professor of Multiscale Modeling of Liquid Materials at the Technische Universität München (TUM), affiliated with the TUM School of Engineering and Design. Her research integrates physical modeling with machine learning and Bayesian techniques to develop multi-scale simulation frameworks for diverse applications in bioinformatics and engineering. Education: She earned her PhD in Physics from the University of Ljubljana (2015) and conducted postdoctoral research at ETH Zurich (2016–2019), where she received an ETH Postdoctoral Fellowship. Since 2019, she has held her current position at TUM. Research Interests: Her work focuses on advancing machine learning potentials, Bayesian uncertainty quantification, and multi-scale modeling for complex systems like ionic liquids, metal-organic frameworks, and biomolecules. Her ERC Starting Grant (2022) supports the SupraModel project, emphasizing scalable and interpretable models. Awards: Golden Teaching Award 2022 (Best Lecture), ERC Starting Grant 2022, and ETH Postdoctoral Fellowship. Her recent publications emphasize neural network potentials, transfer learning, and computational tools like JaxSGMC for Bayesian analysis. Grants and Labs: While specific lab names are not mentioned, her ERC grant underscores active funding. No formal student advisee list is provided, but her collaborative work suggests involvement in training next-generation computational scientists.

Peter Sollichمشاهده پروفایل

استاد

• Non-equilibrium statistical physics
• Soft matter rheology
• Active matter
• +۷ مورد دیگر

Prof. Dr. Peter Sollich is a Professor of Theoretical Physics at Georg-August-Universität Göttingen, affiliated with the Institute for Theoretical Physics. His research spans non-equilibrium statistical physics with applications to soft matter, active systems, and complex networks. He maintains a small part-time appointment at King's College London. His primary research interests focus on non-equilibrium statistical physics , particularly soft and active matter rheology, jamming transitions, glassy dynamics, dynamical phase transitions, and inference from dynamical data. His work bridges theoretical physics with applications in materials science and network theory, emphasizing both fundamental mechanisms and quantitative modeling approaches. Analysis of his recent publications reveals strong thematic consistency in studying glassy dynamics and active matter systems , with increasing integration of machine learning techniques for network analysis. Key methodological threads include coarse-grained modeling, spectral analysis of complex systems, and non-equilibrium thermodynamics frameworks. His 2023-2025 work shows growing emphasis on nonreciprocal interactions in active mixtures and physics-inspired machine learning applications. Prof. Sollich actively supervises Bachelor's, Master's, and PhD students, welcoming thesis inquiries in theoretical physics. His group develops analytical and computational approaches to complex dynamical systems, with recent grants likely supporting work on network dynamics and active matter modeling (specific grants not detailed in source text). His research group operates within the Institute for Theoretical Physics at Göttingen, focusing on computational and analytical modeling of disordered systems. Current projects involve elastoplastic modeling of amorphous solids, spectral analysis of heterogeneous networks, and theoretical frameworks for active matter phase separation.

Dirk Peschkaمشاهده پروفایل

پژوهشگر

• Partial Differential Equations
• Fluid Dynamics
• Materials Science
• +۷ مورد دیگر

Dr. Dirk Peschka is a researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany, where he contributes to the Partial Differential Equations Research Group (FG1) . He is affiliated with the Berlin Mathematics Research Center MATH+ , the Society for Applied Mathematics and Mechanics (GAMM) , and the German Physical Society (DPG) . Research Interests Mathematical modeling of fluid dynamics and materials science using partial differential equations (PDEs). Applications in thin film dynamics, semiconductor devices, and reactive multiphase flows. Development of gradient flow frameworks and thermomechanical models via GENERIC formalism. Analysis of contact line behavior, dewetting processes, and fluid-structure interaction. Numerical methods for semiconductor simulations and geoscience applications. Publications Trends His recent work (2022–2025) emphasizes energy-based modeling of thin films, reactive flows, and semiconductor degradation. Key themes include contact line dynamics, gradient flows, and multiscale analysis of materials and fluid systems. Memberships Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin Mathematics Research Center MATH+ Society for Applied Mathematics and Mechanics (GAMM) German Physical Society (DPG)

• Composite Materials
• Structural Mechanics
• Fatigue Damage Modeling
• +۶ مورد دیگر

Professor Raimund Rolfes is affiliated with the Institute of Statics and Dynamics at Gottfried Wilhelm Leibniz University Hannover, where he leads multiple research initiatives in structural mechanics and composite materials. His research interests focus on advanced computational methods for analyzing composite structures, particularly fiber-reinforced plastics. His work spans multiscale modeling approaches, fatigue damage analysis, structural stability, and digital twin technology for megastructures. His research has significant applications in wind energy, offshore structures, and sustainable aviation. Rolfes currently serves as speaker for Collaborative Research Center SFB 1463 on Offshore Megastructures and participates in excellence clusters including EXC 2163 Sustainable and Energy-Efficient Aviation (SE²A) and GRK 2657 Methods of Numerical Mechanics in Higher Dimensions. His research portfolio includes numerous ongoing DFG-funded projects on composite material behavior, structural monitoring using AI, and digital twin development. His leadership extends to large-scale research equipment projects and priority programs focused on composite material testing and validation. His work bridges theoretical modeling with experimental validation across multiple scales, from nanocomposites to full-scale structural systems.

Axel Sielaffمشاهده پروفایل

پژوهشگر

• Boiling and Evaporation
• Heat Transfer
• Microgravity Applications
• +۳ مورد دیگر

Dr. Axel Sielaff is a Researcher at the Institute for Technical Thermodynamics at Technische Universität Darmstadt since 2008. His work focuses on boiling and evaporation phenomena, particularly in microgravity environments and complex fluids. He has conducted research visits at the Indian Institute of Technology Madras and the Royal Melbourne Institute of Technology. Education: He holds a Diplom in Mechanical Engineering (Energy and Process Engineering) from Leibniz University Hannover. He also completed visiting studies at the Chinese Academy of Sciences (Beijing), Zhuhai MTU Maintenance, and Brunel University, West London. Research Interests: His group investigates two-phase heat transfer, nucleate boiling dynamics, and the effects of microgravity on evaporation. Projects include multiscale experiments on complex fluids, numerical simulations of bubble growth, and applications in spacecraft thermal management via the RUBI experiment on the International Space Station. Key areas include microlayer formation, electric field effects on bubble detachment, and residue formation of fuels/AdBlue under high-velocity airflow conditions. Technical Contributions: Develops experimental setups for sub-millimeter scale analysis and high-precision infrared thermography. Collaborates internationally on projects such as the Multiscale Boiling Investigation (ISS-based) and Deposit Formation Studies. Labs/Teams: Active in the Boiling and Evaporation research group at TU Darmstadt, contributing to both fundamental and applied thermal engineering research.

• Orthopedic Surgery
• Biomechanics
• Bone Regeneration
• +۳ مورد دیگر

Michael Hahn is a researcher at the Institute of Osteology and Biomechanics within the University Medical Center Hamburg-Eppendorf. His work focuses on orthopedic surgery, biomechanics, and medical engineering, particularly in skeletal regeneration and implant stability. Research Interests : Orthopedic biomechanics, bone regeneration, trauma surgery, implantology, and medical technology. Publication Trends highlight interdisciplinary research at the intersection of orthopedics, dentistry, and radiation physics. His work includes studies on allograft integration, pedicle screw stability, transverse tarsal arch mechanics, and radiation effects on bone integrity. Collaborative projects often involve multiscale characterization of biomaterials and clinical translation of implant designs. Infrastructure Affiliation : Linked to UKE's Core Facilities , including research animal husbandry and specialized laboratories. Part of the Neuro-Immune Network Hamburg alliance.

Rudolf Weeberمشاهده پروفایل

مدرس ارشد

• Magnetic Gels and Elastomers
• Soft Matter Simulations
• Computational Physics
• +۶ مورد دیگر

Rudolf Weeber is a Senior Lecturer at the Institute for Computational Physics, University of Stuttgart. He specializes in computational physics and soft matter simulations, with a focus on magnetic gels, elastomers, and nanoparticle dynamics. PhD in Physics (2015) from University of Stuttgart Diplom in Physics with specialization in simulation methods His research explores magnetic field-controlled materials, fluid-particle coupling, and simulation software development. He is a key contributor to the ESPResSo molecular dynamics package. Recent publications highlight his work on magnetic gel deformation, nanoparticle transport, and multiscale simulation frameworks. Trends include computational physics, soft matter, and high-performance computing. Weeber's projects span magnetic materials, micro-rheology, and sustainable software development. He has collaborated on EuroHPC systems and stochastic reaction modeling. He is affiliated with the Stuttgart Research Center for Simulation Technology and has developed open-source tools for soft matter research.

• Uncertainty Quantification
• Predictive Modeling
• Computational Mathematics
• +۵ مورد دیگر

Nicholas Zabaras is a Professor of Uncertainty Quantification and the Director of the Warwick Centre for Predictive Modelling at the University of Warwick. He is a Hans Fischer Senior Fellow at the TUM Institute for Advanced Study (TUM-IAS), hosted by Phaedon-Stelios Koutsourelakis. His research focuses on advancing computational methods for uncertainty quantification, predictive modeling, and multiscale/multiphysics systems. Key areas include Bayesian methods, stochastic modeling, and data-driven approaches for complex materials systems. Education: He holds a diploma in Mechanical Engineering from the National Technical University of Athens (1982), an M.Sc. in Materials Science and Engineering from the University of Rochester (1983), and a Ph.D. in Theoretical and Applied Mechanics from Cornell University (1987). He has held academic positions at the University of Minnesota, Cornell University, and the University of Warwick, where he now leads the Warwick Centre for Predictive Modelling. Research Interests: His work integrates computational mathematics, statistics, and scientific computing to address challenges in materials science, computational physics, and engineering systems. Specific themes include Bayesian uncertainty quantification, high-dimensional modeling, information-theoretic coarse graining, and stochastic model reduction. Awards: He has received the Royal Society Wolfson Research Merit Award (2014), the Michael Tien’72 College of Engineering Teaching Award (2009), and is a Fellow of the American Society of Mechanical Engineers (2006). Labs/Teams: Director of the Warwick Centre for Predictive Modelling, and leader of the Scientific Computing and Artificial Intelligence (SCAI) Laboratory at the University of Notre Dame, focusing on interdisciplinary research in AI-driven predictive modeling and uncertainty quantification.

• Materials Science
• Energy Storage
• Nanotechnology
• +۳ مورد دیگر

Saibal Jana is a researcher at the Karlsruhe Institute of Technology (KIT) within the Multiscale Materials Modelling and Virtual Design research unit at the Institute of Nanotechnology. His work focuses on computational modeling and experimental analysis of advanced materials for energy storage, particularly in lithium-ion and calcium batteries, utilizing techniques like molecular dynamics simulations and spectroscopy. Research Areas: Energy storage materials, metal-organic frameworks, electrochemical modeling, and visible-light catalysis. Email: jana.saibal@kit.edu Recent publications highlight his contributions to understanding solid electrolyte interphase growth, designing redox-active cathodes, and exploring molecular self-assembly mechanisms. His work aligns with initiatives like BATTERY 2030+ for next-generation battery technology.

Britta Nestlerمشاهده پروفایل

استاد

• Materials Science
• Computational Materials Science
• Phase-Field Modeling
• +۳ مورد دیگر

Prof. Dr. Britta Nestler serves as a Research Unit Chair at the Institute of Nanotechnology (INT) within the Karlsruhe Institute of Technology (KIT), Germany. Leading the Microstructure Simulations research group (INT-MSS), she focuses on computational modeling of mechanical and microstructural properties in materials, with significant contributions to phase-field methodologies for microstructure evolution and materials design. Her research spans computational materials science, phase-field modeling, and multiphysics simulations for energy storage systems. Key interests include chemo-mechanical coupling in multiphase systems, solid-state dewetting phenomena, battery electrode optimization, and microstructure-property relationships in polycrystalline materials. She integrates machine learning and data management frameworks to advance virtual materials design, particularly for post-lithium battery technologies. Recent publications reveal a strong emphasis on phase-field applications for energy materials, with 15+ 2025 articles addressing battery electrode design, structural optimization of porous materials, and multiphysics coupling in electro-chemo-mechanical systems. Her work bridges fundamental thermodynamics with industrial applications, notably in the POLiS Cluster of Excellence for post-lithium storage. Prof. Nestler actively shapes the field through leadership in the GAMM Workshop on phase-field modeling and the Materials/Microstructure Modeling conference. As part of KIT's Institute of Nanotechnology, her INT-MSS group collaborates on virtual materials design initiatives within the MaTeLiS Focus Field and NFDI4Ing research data infrastructure, driving digitalization in engineering sciences.