جست‌وجوی استاد و پژوهشگر

Vitus Bensonمشاهده پروفایل

پژوهشگر

• Deep Learning
• Biogeochemistry
• Climate Risk Mitigation
• +۴ مورد دیگر

Vitus Benson is a doctoral researcher at the Max Planck Institute for Biogeochemistry and ETH Zürich, focusing on deep learning applications in Earth observation and climate science. As part of the EarthNet project and AI4Carbon Community Initiative , he develops AI systems for climate risk mitigation and carbon cycle modeling. Current Affiliation: ELLIS PhD Student, Max Planck Institute for Biogeochemistry & ETH Zürich Education: MSc Mathematical Physics (University of Leipzig, PIK Potsdam, University of Stockholm, St. Petersburg State University), BSc Mathematics & Business Administration (University of Göttingen) His research interests span: Deep learning for Earth system data Biogeochemical cycle modeling Climate change adaptation strategies Complex systems analysis Early warning systems development Atmospheric transport modeling Analysis of his publications reveals trends in: Scientific machine learning for climate systems Integration of multimodal geospatial data Carbon cycle modeling with neural networks Vegetation forecasting using spatiotemporal methods Climate risk mitigation through AI Teleconnection pattern analysis for weather prediction As a key contributor to the EarthNet initiative , he works on data-driven impact early warning systems for climate extremes. His collaborations include Markus Reichstein and Fanny Yang , with active participation in climate science workshops and symposia.

• Land-atmosphere interactions
• Climate-carbon cycle system
• Machine learning in Earth system research
• +۵ مورد دیگر

Dr. Alexander Winkler serves as a Group Leader in the Department of Biogeochemical Integration at the Max Planck Institute for Biogeochemistry in Jena, Germany. His research focuses on the complex interactions between the atmosphere and biosphere, particularly examining energy, water, and carbon exchanges within the Earth system. He leads the research group on Atmosphere-Biosphere Coupling, Climate and Causality, and contributes significantly to the USMILE ERC Project that explores statistical and machine learning applications in Earth system research. Winkler's research interests span land-atmosphere interactions, climate-carbon cycle feedbacks, and the application of modern statistical and machine learning methods to Earth system science. His work particularly emphasizes hybrid modeling approaches that combine process-based and data-driven models to better understand land-atmosphere interactions. He investigates how rising atmospheric CO 2 concentrations affect Earth observations and model simulations through causal inference techniques, and examines constraints on key entities in the climate-biosphere continuum by linking multi-model ensemble simulations with observational data. His recent publications reveal a strong trend toward integrating machine learning with traditional Earth system modeling, with significant contributions in carbon cycle dynamics, climate-carbon feedbacks, and vegetation responses to climate change. His research demonstrates how hybrid modeling approaches can enhance our understanding of complex Earth system processes, particularly in disentangling the effects of rising CO 2 on land-atmosphere exchanges of carbon and water. Winkler actively contributes to academic education, teaching courses such as 'Klimatologie und Klimawandel' at Friedrich-Schiller-Universität Jena's Chemisch-Geowissenschaftliche Fakultät, and has previously taught 'Terrestrial Ecosystem Processes & Carbon Feedbacks within Earth System Models' at Universität Hamburg. His teaching reflects his research expertise, focusing on the intersection of climate science, carbon cycle dynamics, and advanced data analysis techniques.

Fabian Weinمشاهده پروفایل

پژوهشگر

• Topology Optimization
• Structural Optimization
• Computational Mathematics
• +۳ مورد دیگر

Dr. Fabian Wein serves as a Senior Scientist at the Chair of Applied Mathematics (Continuous Optimization) within the Department of Mathematics at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). His research focuses on advanced optimization techniques with applications in structural design and additive manufacturing. Wein maintains an active research profile with numerous publications in leading optimization journals and collaborates extensively within Michael Stingl's research group. Wein's research interests center on topology optimization methodologies, particularly feature-mapping approaches for structural optimization. His work bridges theoretical mathematics with practical engineering applications, especially in the context of additive manufacturing technologies. He has made significant contributions to two-scale optimization of graded lattice structures, buckling analysis of cellular structures, and optimization of piezoelectric devices. His research demonstrates a consistent focus on developing computationally efficient methods that respect manufacturing constraints while achieving optimal structural performance. Analysis of Wein's recent publications reveals a strong trend toward multiscale optimization approaches that integrate microstructural design with macroscopic performance requirements. His work increasingly addresses the challenges of additive manufacturing, particularly in developing optimization frameworks that account for process-structure-property relationships. The research spans from fundamental mathematical developments to practical applications in electromechanics, fluid dynamics, and structural mechanics. Wein actively participates in academic advising and research supervision, as evidenced by his involvement with master's theses such as 'Optimization of a Solar Air Heater' (2022). His research has received substantial citations, indicating significant impact in the optimization community. The work is often supported through collaborations with institutions like the Max Planck Society and Fraunhofer Institutes, leveraging FAU's strong research ecosystem. Within the research environment at FAU, Wein contributes to the Continuum Optimization group that maintains several computational resources including openCFS (an open-source C++ FEM framework with structural optimization capabilities) and iTop (an interactive educational topology optimization platform). His work connects with broader university initiatives in advanced manufacturing and computational engineering, contributing to FAU's position as one of Germany's leading research universities with over 14,000 staff members across the Nuremberg Metropolitan Region.

Shmuel Avidanمشاهده پروفایل

استاد

• Object Tracking
• 3D Object Modeling
• Computer Vision
• +۳ مورد دیگر

Prof. Shmuel Avidan serves as a Professor in the School of Electrical Engineering at Tel Aviv University's Iby and Aladar Fleischman Faculty of Engineering. Holding a Ph.D. from Hebrew University's School of Computer Science (1999), he brings extensive industry experience from Adobe, Mitsubishi Electric Research Labs, MobilEye, and Microsoft Research to his academic role. His educational trajectory features: Ph.D. in Computer Science, Hebrew University of Jerusalem (1999) Avidan's research centers on pixel-centric computational problems, with seminal contributions in video object tracking and 3D object modeling from 2D images. His work spans computer vision, image processing, and machine learning, emphasizing practical applications in industrial settings. Current investigations explore neural rendering, foundation models, and diffusion-based architectures for visual understanding. Recent publications (2023-2025) demonstrate concentrated innovation in neural radiance fields (NeRF), category-agnostic pose estimation, and texture-aware segmentation. These works increasingly integrate foundation models with domain-specific applications in medical imaging, autonomous systems, and materials science, reflecting a strategic shift toward scalable vision systems. Though specific awards aren't documented in source materials, his prolific publication record and sustained industry partnerships signify substantial field impact. His research group maintains active collaboration with leading technology firms, translating academic discoveries into real-world solutions. Professor Avidan mentors graduate students in computer vision while securing competitive grants for projects at the intersection of theoretical computer vision and industrial implementation. His lab focuses on developing robust algorithms for challenging visual environments, particularly in autonomous driving and medical imaging contexts. Leading an active research group within Tel Aviv University's Electrical Engineering department, he drives innovation in neural rendering and vision-language models. The team regularly contributes to premier conferences including CVPR, ICCV, and ECCV, maintaining strong industry ties through ongoing partnerships with automotive and imaging technology companies.

• Autonomous Vehicles
• Reachability Analysis
• Temporal Logic
• +۳ مورد دیگر

Florian Lercher is a Researcher at the Department of Cyber Physical Systems at the Technical University of Munich (TUM). He joined the group led by Prof. Matthias Althoff in April 2023 as a PhD student and research assistant. His work focuses on motion planning for autonomous vehicles, particularly integrating reachability analysis and temporal logic for specification-compliant driving corridors. Education: B.Sc. in Informatics (TUM, 2020), M.Sc. in Software Engineering (TUM, LMU Munich, and University of Augsburg, 2022) Research: Traffic rule-compliant motion planning, hybrid systems verification, and formal methods in autonomous driving Teaching: Lectures and practical courses on AI fundamentals and formal methods for cyber-physical systems His recent publications emphasize efficient driving corridor extraction, temporal logic simplification, and model checking for hybrid systems. He actively supervises student theses on topics like SAT-based refinement guidance and inverse reinforcement learning for autonomous vehicles.

Frank Meyer zu Heringdorfمشاهده پروفایل

استاد

• Condensed Matter Physics
• Materials Science
• Ultrafast Dynamics
• +۵ مورد دیگر

Prof. Dr. Frank Meyer zu Heringdorf is a Principal Investigator at the Collaborative Research Centre 1242 (University of Duisburg-Essen) and holds the Professor rank. His work focuses on ultrafast electron dynamics , spin-state switching , and X-ray spectroscopy in condensed matter systems. Affiliation: University of Duisburg-Essen, Germany Research Focus: Electron correlations, magnetic materials, and nanoscale physics Research Interests: Meyer zu Heringdorf explores collective electronic excitations , non-equilibrium energy transfer , and photoinduced magnetism using advanced techniques like femtosecond laser spectroscopy and soft X-ray absorption . His group investigates spin crossover complexes , magnetocaloric materials , and electron-phonon interactions in heterostructures. Article Trends: Recent publications highlight table-top X-ray sources , renormalized charge transfer in oxides, graphene-mediated magnetic anisotropy , and attosecond pulse synthesis . These works span ultrafast dynamics , nonlinear absorption , and quantum material design . Labs & Collaborations: He leads Project B06 in CRC 1242, collaborating with researchers across European XFEL , nickel and iron compounds , and multiferroic systems .

Mohammad Sadeghiمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Haptics
• Finite Element Method (FEM) Simulation
• +۳ مورد دیگر

Mohammad Sadeghi is a Research Fellow at the Institute of Mechatronics in Mechanical Engineering, Hamburg University of Technology, specializing in robotics, haptic systems, and sensor design. He holds a Dr.-Ing. degree and maintains active roles in research supervision and collaborative industry projects. His academic foundation includes: M.Sc. in Mechanical Engineering from Ferdowsi University, Mashhad, Iran B.Sc. in Mechanical Engineering from Birjand University, Birjand, Iran Dr. Sadeghi's research integrates robotics with medical applications, focusing on wearable exoskeletons for telerehabilitation, bio-inspired whisker sensors, and magnetoelectric actuator systems. His methodological approach combines finite element simulation, inverse optimization, and machine learning to solve challenges in human-robot interaction and precision sensing. Recent work demonstrates growing emphasis on mixed reality integration for industrial haptic feedback and minimally invasive surgical applications. Publication analysis reveals dual research thrusts: medical robotics (40% of recent output) featuring rehabilitation devices and surgical sensors, and magnetoelectric systems (35%) for bioimaging and precision actuation, complemented by biomaterials and manufacturing optimization studies. Recognition includes: Hamburg Innovation Call for Transfer award (2024) for surgical precision enhancement via whisker sensors He supervises six active theses on topics including magnet-based whisker sensors and mixed reality haptic feedback, with Kyrillos Adeeb's 2024 singularity-avoidance project completed. Industry collaborations with Schaeffler (robotic manufacturing), MBJ GmbH (solar robotics), and bAhead (liquid handling systems) demonstrate translational impact. His experimental work utilizes the institute's Haptics Lab and PHiLsLab for hardware-in-the-loop validation of autonomous systems.

• Geophysics
• Seismology
• Tectonics
• +۳ مورد دیگر

Sébastien Chevrot is a Researcher at the Institut de Recherche en Astrophysique et Planétologie (IRAP) , Université Paul Sabatier, Toulouse, France. His work focuses on geophysical tomography to study lithospheric structures in orogenic zones like the Pyrenees and Massif Central. Key Research Areas: Crustal and mantle imaging, lithospheric segmentation, seismic wave propagation, and tectonic inversion of continental rifts. The 2014 tomographic study co-authored by Chevrot utilized data from the PYROPE and IBERARRAY experiments to resolve deep crustal and mantle structures. This work challenged subduction models for the Pyrenees and highlighted the role of reactivated Hercynian faults. Crustal corrections were computed using receiver functions and seismic profiles, improving resolution of lithospheric heterogeneities. The study demonstrated that uncorrected crustal anomalies can vertically smear tomographic images, emphasizing the importance of integrating detailed crustal models in regional seismic studies.

Olga Shishkinaمشاهده پروفایل

پژوهشگر

• Turbulent Convection
• Fluid Dynamics
• Heat Transfer
• +۳ مورد دیگر

PD Dr. Olga Shishkina is a Max Planck Research Group Leader at the Laboratory for Fluid Physics, Pattern Formation and Biocomplexity (LFPB) within the Max Planck Institute for Dynamics and Self-Organization in Göttingen, Germany. Her research focuses on turbulent thermal convection, numerical simulations, and heat/momentum transport in complex fluid systems. Fields of Interest : Turbulent Convection, Fluid Dynamics, Heat Transfer, Numerical Simulations, Thermal Boundary Layers, Scientific Computing Dr. Shishkina's work explores turbulent Rayleigh-Bénard convection, magnetoconvection, and rotating/centrifugal convection through direct numerical simulations (DNS). Key themes include scaling relations, boundary layer dynamics, and the impact of geometric constraints, magnetic fields, and buoyancy forces on flow structures. Her recent publications (2024–2025) analyze scaling laws in sheared/rotating convection, heat transport in annular/cylindrical geometries, and machine learning applications for cosmological simulations. The group also investigates liquid metal convection, subcritical chaos, and superstructures in confined turbulent flows. Contact : +49 551 5176-335 | +49 551 5176-302 | olga.shishkina@ds.mpg.de Address : Room 2.124, Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077 Göttingen, Germany

Miriam Schulteمشاهده پروفایل

استاد

• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• Scientific Computing
• +۵ مورد دیگر

Miriam Schulte is a Professor at the Institute for Parallel and Distributed Systems (IPVS) at the University of Stuttgart . As Dean of Studies SimTech , she leads academic programs in simulation technology. Her research focuses on high-performance computing , multi-physics simulations , and scientific software development , with significant contributions to coupling libraries like preCICE and biophysical frameworks like OpenDiHu . Key Research Areas: High-Performance Computing (HPC) Multi-physics and Fluid-Structure Interaction (FSI) Sparse Grids and Hierarchical Numerical Methods Machine Learning in Simulation Software Parallel and GPU-Accelerated Algorithms Advising: Guided student projects on quantum neural networks , GPU-optimized sparse grids , and SYCL-based HPC frameworks . Coordinated SimTech Research Modules and IPVS/SGS team initiatives. Software Leadership: Maintains preCICE (coupling library for multi-physics) Develops OpenDiHu (neuromuscular simulations) Advances PLSSVM (parallel SVM library) and SG++ (sparse grids) Her recent publications (2022–2025) emphasize machine learning integration with multi-physics simulations , including groundwater heat pumps , brain tumor modeling , and neuromuscular EMG prediction . She actively promotes open-source software sustainability and collaborative research infrastructure at the University of Stuttgart.

Jun Zhangمشاهده پروفایل

مربی

• Environmental Sensing
• Laser Sensors
• Greenhouse Gas Monitoring
• +۳ مورد دیگر

Jun Zhang, M.Eng., serves as a Tutor at the Technical University of Munich (TUM) under the Professorship for Environmental Sensors and Modeling. His work focuses on portable laser-based sensor development and inverse modeling of urban CO 2 emissions, aligning with TUM's research in environmental monitoring and atmospheric science. Education : Master of Engineering (M.Eng.) Research Interests : Portable laser-based calibration-free gas sensor development Inverse estimation of urban CO 2 emissions using surface and column observations Teaching : Tutor - Advanced Seminar Environmental Sensing Tutor - Joint practical course: Electromagnetic Sensors and Measurement Systems Awards : Hans Fischer Fellowship Contact: Room N2307, Phone +49 (89) 289 23328

Mario Ohlbergerمشاهده پروفایل

استاد

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +۳ مورد دیگر

Prof. Dr. Mario Ohlberger is a faculty member in the Department of Mathematics and Computer Science at the University of Münster, Germany. As a leading researcher in numerical analysis and scientific computing, he contributes to the Cluster of Excellence 2044 'Mathematics Münster: Dynamics – Geometry – Structure' and the Mathematical Research Data Initiative (MaRDI). His work integrates machine learning with classical numerical methods. University: University of Münster Research Focus: Numerical analysis for PDEs, model reduction, machine learning, multiscale methods Key Projects: EXC 2044 (subprojects C2 and C4), MaRDI His recent publications emphasize adaptive reduced basis methods, multi-fidelity learning, and surrogate modeling for parameterized PDEs, with applications in oil recovery and reactive transport. He has supervised numerous theses and taught courses on numerical analysis, scientific computing, and model reduction. Scientific awards include the Eliteförderprogramm für Postdoktoranden (2002), Ferdinand-von-Lindemann-Preis (2001), and a 1994 teaching award. Current teaching includes lectures on differential equations and seminars on advanced numerical methods.

Benedikt Wirthمشاهده پروفایل

استاد

• Optimisation and calculus of variations
• image processing
• scientific computing
• +۹ مورد دیگر

Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.

• Calculus of Variations
• Nonlinear Partial Differential Equations
• Materials Science
• +۵ مورد دیگر

Prof. Dr. Carolin Kreisbeck is a full professor holding the Chair of Analysis in the Department of Mathematics at the Faculty of Mathematics and Geography, Catholic University of Eichstätt-Ingolstadt. She serves as Dean of the Faculty since October 2023 and maintains active research collaborations across Europe. Her research focuses on nonlinear partial differential equations and calculus of variations, with particular expertise in: Variational multiscale problems and relaxation techniques Applications in materials science (elasticity, plasticity, metamaterials) Nonlocal operators and fractional calculus Image processing and machine learning applications Gamma-convergence and homogenization theory Her recent publications (2022-2025) demonstrate strong emphasis on nonlocal gradient theories, materials modeling, and variational methods for multiscale problems. The works consistently integrate rigorous mathematical analysis with applications in physics and engineering sciences. Awards and recognitions include: NWO TOP Grant for postdoc position (2019-2021) Multiple teaching excellence awards (University of Regensburg 2013, Utrecht BKO 2016) Prestigious scholarships (Westerdijk, TopMath, Oberwolfach) EON Culture Prize Bavaria (2011) She holds significant professional roles including Associate Editor for Advances in Continuous and Discrete Models , GAMM committee leadership, and organization of international mathematics conferences.

Harrie-Jan Hendricks-Franssenمشاهده پروفایل

استاد

• Hydrology
• Hydrological Modeling
• Data Assimilation
• +۴ مورد دیگر

Harrie-Jan Hendricks-Franssen is a Professor at the Jülich Research Centre , affiliated with the Institute of Bio- and Geosciences (Agrosphere, IBG-3). He serves as a Principal Investigator in the Cosmic Sense team, focusing on advanced hydrological modeling and data assimilation techniques. Research Expertise : Development of inverse modeling methods for subsurface and land surface hydrological systems, integration of terrestrial system modeling, high-performance computing, and statistical analysis of hydrological data (e.g., neutron intensity measurements via cosmic ray probes). Technical Contributions : Implementation of measurement operators linking neutron intensity to soil moisture distribution, enabling precise environmental monitoring. Contact details: Tel. +49 2461 61 4462, Fax +49 2461 61 2518, Address: Wilhelm-Johnen-Straße, 52428 Jülich, Germany.