Computational Inverse Problems Professors and Supervisors in Germany

Olga Shishkinaمشاهده پروفایل

پژوهشگر

Max Planck Institute for Dynamics and Self-OrganizationGermany

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

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Miriam Schulteمشاهده پروفایل

استاد

University of StuttgartGermany

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.

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Mario Ohlbergerمشاهده پروفایل

استاد

University of MünsterGermany

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.

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Benedikt Wirthمشاهده پروفایل

استاد

University of MünsterGermany

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.

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Thao Nguyen Hieuمشاهده پروفایل

مدرس

University of GöttingenGermany

Dr. Thao Nguyen Hieu serves as a Lecturer in IT at RMIT University Vietnam's Faculty of Science, Engineering & Technology, based at the Saigon South Campus. With higher education experience spanning six universities across four countries, he brings substantial international expertise to his role. His research interests focus on Variational Analysis , Optimization Algorithms , Inverse Problems in Imaging , and Convolutional Neural Networks , representing a strong intersection of mathematical theory and practical applications in data science. His teaching portfolio includes Introduction to Programming (Python), Mathematics for Computing, and Statistical Signal Processing at the Masters level. German-Israeli Foundation Scholarship (2015-2017) Australian Research Council Scholarship (2011-2015) Special Overseas Student Scholarships (SOSS), Australia (2011-2015) Student Travel Award, Federation University Australia (2012) Best oral presentation 1st prize award (2013) Odon Vallet Scholarship (2004) Two bronze Medals in Mathematics Olympiad (2003, 2004) Dr. Hieu has supervised numerous graduation theses for math and engineering students and maintains active research connections through projects like Metrology Advances for Digitized Electronic Components and Systems Industry 4.0 (2019-2021) and Integrating Real-time and Post-processing Image Restoration (2017-2019).

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Oleg Davydovمشاهده پروفایل

استاد

Justus-Liebig-University GiessenGermany

Professor Oleg Davydov holds a Professorship for Numerical Analysis at the Department of Mathematics, University of Giessen, Germany. His research focuses on developing advanced numerical methods with strong theoretical foundations and practical applications. He maintains an active research program with numerous recent publications and international collaborations. Position: Professor of Numerical Analysis Institution: University of Giessen, Department of Mathematics Contact: Heinrich-Buff-Ring 44, 35392 Giessen, HRZ Room 117 Email: oleg.davydov@math.uni-giessen.de Homepage: https://oleg-davydov.de/ Professor Davydov's research interests center around meshless numerical methods, approximation theory, and computational mathematics. His primary focus areas include: Meshless Finite Difference Method - Developing robust meshless techniques that avoid the need for structured grids Finite Element Method - Particularly Bernstein-Bézier finite elements and specialized approaches for complex geometries Scattered Data Fitting - Creating efficient algorithms for approximating data on irregular domains Approximation Theory - Investigating theoretical properties of splines, radial basis functions, and other approximation tools His research has resulted in several software packages including mFDlab (Meshless Finite Difference Method), BBFEM (Bernstein-Bézier Finite Elements), and TSFIT (Two-Stage Scattered Data Fitting), demonstrating the practical implementation of his theoretical work. Analysis of Professor Davydov's recent publications shows a consistent focus on improving meshless methods, particularly in stencil selection, error analysis, and applications to complex problems. His work spans both theoretical developments (like error bounds and optimal approximation orders) and practical implementations (for fluid dynamics, manifold learning, and interface problems). A notable trend is the increasing sophistication of adaptive techniques and the handling of challenging geometries. Professor Davydov has supervised several doctoral students to completion, including: Gaelle Andriamaro Fabien Rabarison Abid Saeed Wee Ping Yeo His research group appears to maintain active collaborations with institutions worldwide, as evidenced by his extensive co-authorship network. The group focuses on developing both theoretical foundations and practical implementations of numerical methods, with particular attention to problems involving irregular domains, singularities, and complex geometries. Students in his group would gain experience in both theoretical analysis and software development for numerical methods.

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Miriam Primbsمشاهده پروفایل

استاد

Hochschule Ruhr WestGermany

Professor Miriam Primbs is a faculty member at Hochschule Ruhr West (HRW) in the Institute of Natural Sciences, where she teaches Mathematics and Simulation. She has been a professor at HRW since 2011 and coordinates the HRW research focus MARTA since 2022. Professor Primbs studied Mathematics and Physics (Diplom and 1. Staatsexamen) and earned her PhD in Mathematics at the University of Duisburg-Essen. Her career includes a research stay at IMATI CNR in Italy, teaching on the school ship Thor Heyerdahl, and working in Research and Development at Siemens Energy Mülheim before joining HRW. This blend of academic and industry experience informs her teaching and research approach. Her research focuses on applying mathematical and numerical methods to technical problems across engineering disciplines, from mechanics to electrical engineering. As coordinator of the MARTA research focus, she leads various projects including those funded by BMBF and collaborations with industry partners like Siemens Energy. Her scholarly work spans wavelet analysis, numerical methods, signal processing, and power systems analysis. Professor Primbs teaches Engineering Mathematics I and II, Mathematics for E-Commerce, MATLAB, Scientific Simulation, and LaTeX Basics. She employs a flipped classroom approach for foundational courses, providing students with tailored materials and prompt feedback through weekly exercises. In higher semesters, she emphasizes project-based learning and specialized topics. Currently supervising doctoral student Martin Wachs in cooperation with University of Duisburg-Essen Member of the Senate Committee for Research and Transfer at HRW Encourages industry collaboration for practical research applications Professor Primbs maintains strong connections between academic research and industrial applications, leveraging her Siemens Energy experience to bridge theory and practice in mathematical engineering.

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Korbinian Schneebergerمشاهده پروفایل

استاد پژوهشی

Max Planck Institute for Plant Breeding ResearchGermany

Prof. Dr. Korbinian Schneeberger is a Research Professor and Group Leader at the Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research in Cologne, Germany. His research focuses on genome plasticity and computational genetics, developing cutting-edge technologies to explore genome evolution. He leads a multidisciplinary team consisting of bioinformaticians, biologists, and biotechnologists driven by curiosity and passion for new genomic technologies. Dr. Schneeberger's research group pioneers genomic biotechnology for crop biology, addressing real-world problems in genomics-assisted science and breeding using Arabidopsis, potato, and fruit trees as model systems. They study genomic differences, how to find and describe them, and their relevance to phenotype. The group specializes in single-cell sequencing technologies to understand controlled (recombination) and uncontrolled (mutations) events that drive genomic diversity. His research spans four main areas: Optimizing Plant Performance by Mapping the Interface between Development and Metabolism, Plant Microbiota Metabolic Networks and Edaphic Adaptation, Synthetic and Reconstruction Biology, and Theoretical Plant Biology and Data Science. The group has developed several important bioinformatics tools including SyRI for finding genomic rearrangements, plotsr for visualization, SHOREmap for mapping-by-sequencing, and findGSE for genome size estimation. Genome Assembly: Developing new methods for genome assembly, particularly using long-read sequencing data Genomic Variation: Studying mutation accumulation and its role in evolution and adaptation Computational Genetics: Bridging genotype and phenotype using next-generation sequencing Association Studies: Creating methods that associate morphological differences to genomic differences Dr. Schneeberger's recent publications focus on somatic mutations in fruit trees, pan-genome analysis of Arabidopsis, and chromosome-scale genome assembly of potato. His work has significant implications for understanding plant evolution and improving crop breeding strategies. He mentors several PhD students and postdocs, with alumni who have gone on to successful careers in academia and industry. His laboratory maintains the Plant Metabolism and Metabolomics Facility and an Imaging Platform.

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