جستجوی استاد راهنمای دانشگاهی

Janis Nötzelمشاهده پروفایل

پژوهشگر

• Quantum Information Theory
• Machine Learning
• Sensor Networks
• +۳ مورد دیگر

Dr. Janis Nötzel is a senior researcher at the Chair of Theoretical Information Technology (Technische Universität München) and leads his independent Emmy Noether research group. Previously, he held a postdoctoral position at Universitat Autónoma de Barcelona and contributed to 5G practical implementations at TU Dresden's 5G Lab. His research spans quantum information theory, physical layer security, and machine learning applications. Key focuses include Quantum channel capacities under adversarial conditions Entanglement-assisted communication Quantum software frameworks (QuNetSim, QuReed) Interplay between classical and quantum communication Security analysis for 6G networks Resource optimization in quantum systems Recent publications (2023-2025) showcase innovations in Quantum satellite communication architectures Hybrid quantum-classical clustering algorithms Photonic processor instability modeling Covert capacity of compound channels Quantum key distribution resilience Free-space Bessel beam communication He actively collaborates with 6G-life research hub and contributes to quantum network simulation tools. Grants include funding from DFG (Leibniz Program), BMBF (6G-life, Q.Link.X), and StMWi (6G Zukunftslabor Bayern).

• Quantum Information
• Entanglement Theory
• Quantum Gases
• +۵ مورد دیگر

Anna Sanpera Trigueros is a Research Professor at the Universitat Autònoma de Barcelona (UAB) and affiliated with the Department of Physics. Since 2005, she has been an ICREA research professor, contributing to quantum information theory and related fields. Education: PhD in Physics from UAB (1992) Postdoctoral Experience: Oxford University, CEA-Saclay, Leibniz University Her research spans Quantum Information , focusing on: Entanglement theory Quantum phase transitions Quantum neural networks Open quantum systems Recent publications highlight trends in quantum simulation, multi-partite entanglement, and hybrid quantum technologies. Key keywords include Quantum Information , Condensed Matter , and Quantum Optics . She actively engages in science popularization and education, mentoring PhD, master's, and TFG students. Her work intersects theoretical physics, quantum computing, and experimental validation through collaborations.

Benno Liebchenمشاهده پروفایل

استاد

• Soft Matter Physics
• Active Matter
• Colloidal Systems
• +۳ مورد دیگر

Prof. Benno Liebchen holds a faculty position at the Technische Universität Darmstadt within the Institute for Condensed Matter Physics , part of the Faculty of Physics. He leads the Liebchen Group , dedicated to advancing research in the Theory of Soft Matter , focusing on active matter, colloidal systems, and non-equilibrium phenomena. His work explores collective behavior in self-propelled particles, phase transitions in active fluids, and adaptive strategies in smart materials. Research Interests include: Active matter dynamics and pattern formation Non-equilibrium statistical mechanics Biophysical systems and biomimetic design Computational modeling of soft matter Recent publications highlight breakthroughs in intelligent active particles , self-reverting vortices , and motility-induced phase coexistence . His lab develops tools like the AMEP Python package to analyze active systems. Teaching responsibilities include advanced modules in soft matter physics. Collaborative projects involve interdisciplinary approaches to microswimmer behavior and machine learning-driven optimization of collective systems. Contact: +49 6151 16-24509 / Office: S2|04 104

Muhammad Arslan Ansariمشاهده پروفایل

پژوهشگر

• Hybrid Quantum-Classical Computing
• Quantum Computing
• High Performance Computing (HPC)
• +۳ مورد دیگر

Muhammad Arslan Ansari is a researcher at the Department of Computer Architecture and Parallel Systems, Technical University of Munich. He contributes to hybrid quantum-classical computing models and high-performance computing (HPC) integration. Research Focus: Hybrid Quantum-Classical Computing, HPC-QC Integration, Compilation Toolchain Teaching: Seminar leadership on quantum-HPC integration and performance modeling in SS2023-SS2025 Projects: Involved in EuroHPC initiatives like SEANERGYS, PlasmaPEPS, and MUNIQC-ATOMS Software: QMPI, LAIK, and other HPC toolchain developments His publications address quantum accelerator integration challenges and programming tool reviews. He participated in conferences like IEEE QCE 2023.

• Geometric Deep Learning
• Categorical Deep Learning
• Robust Machine Learning
• +۱ مورد دیگر

Aman Saxena is a researcher at the Department of Computer Science in the TUM School of Computation, Information and Technology at Technical University of Munich. His work focuses on geometric/categorical deep learning, robust machine learning, and quantum machine learning. Education: M.Sc. Computational Sciences and Engineering (2019-2023) Location: Boltzmannstr. 3, 85748 Garching b. Munich, Germany (Room 00.11.062) Research Interests: Geometric/Categorical Deep Learning Robust Machine Learning Quantum Machine Learning Bayesian Learning Efficient Machine Learning Code Analysis Recent Publications: Certifiably Robust Encoding Schemes (IEEE International Conference on Quantum Computing and Engineering - QCE 2024) Discrete Randomized Smoothing Meets Quantum Computing (IEEE International Conference on Quantum Computing and Engineering - QCE 2024)

Tommaso Calarcoمشاهده پروفایل

استاد

• Quantum Optimal Control
• Quantum Computing
• Quantum Technologies
• +۳ مورد دیگر

Tommaso Calarco serves as Director of the Institute for Quantum Control (PGI-8) at Jülich Research Centre, leading cutting-edge research in quantum optimal control methodologies for next-generation quantum technologies. His work focuses on developing transformative computational frameworks applicable to natural sciences, logistics, and high-performance computing through advanced quantum device engineering. His research spans quantum optimal control for computation and many-body systems, emphasizing physical model development, model reduction techniques, and machine learning integration for scalable quantum hardware. Key focus areas include spin-qubit optimization, diamond quantum register engineering, and error suppression in gate operations, with significant contributions to ultracold atom systems and semiconductor-based quantum platforms. Analysis of his 2024-2025 publications reveals concentrated efforts on hardware-specific challenges across multiple quantum modalities: spin shuttling fidelity in semiconductor systems, gate optimization for nitrogen-vacancy centers, and photon-spin interface engineering. This work demonstrates a unifying thread of optimal control solutions tailored to platform-specific decoherence mechanisms and scalability constraints. As Director of PGI-8 within the Peter Grünberg Institut, Calarco oversees a dedicated research team advancing quantum control theory and applications, contributing substantially to European quantum technology roadmaps including the Quantum Flagship initiative and strategic European Commission reports.

• Condensed Matter Physics
• Quantum Materials
• Superconductivity
• +۴ مورد دیگر

Prof. Dante Kennes is a University Professor at RWTH Aachen University, leading the Chair of Theoretical Physics of Condensed Matter. His research focuses on quantum materials, strongly correlated systems, and cavity quantum electrodynamics. Key areas include superconductivity in twisted bilayer systems, moiré heterostructures, and non-equilibrium phenomena in low-dimensional materials. He explores theoretical frameworks such as functional renormalization group methods and topological phase transitions. Recent work emphasizes cavity-coupled systems, light-induced superconductivity, and the interplay between electronic correlations and topological properties. His publications address topics like van Hove singularity heterogeneity in graphene, nematicity in kagome metals, and experimental signatures of moiré-engineered phases. Kennes' research bridges theoretical predictions with experimental observability through advanced modeling techniques. His contributions span advanced computational methods for many-body systems and proposals for novel quantum materials characterization. Despite his prolific output, no formal student advisees or awards are explicitly listed in the provided materials.

Kristel Michielsenمشاهده پروفایل

استاد

• Quantum Computing
• Quantum Annealing
• Quantum Simulation
• +۳ مورد دیگر

Prof. Dr. Kristel Michielsen is a Professor of Quantum Information Processing at RWTH Aachen University and holds leadership roles at Forschungszentrum Jülich. She leads the division HPC for Quantum Systems and heads the Jülich UNified Infrastructure for Quantum computing (JUNIQ). As Group Leader of the Research Group Quantum Information Processing, her work focuses on quantum annealing, quantum simulation, and modular hybrid HPC-quantum computing. She is also Spokesperson of the Helmholtz Information Program 1 and a Principal Investigator in Topics 1 and 2. Affiliations: RWTH Aachen University, Forschungszentrum Jülich (IAS/JSC), JUNIQ Key Roles: Head of HPC for Quantum Systems, JUNIQ Director, Quantum Information Processing Group Leader Her research emphasizes quantum computing applications in optimization, benchmarking, and hybrid supercomputing-quantum systems. She pioneers quantum-annealing solutions for real-world problems like power grid partitioning and transportation logistics. Recent work explores error mitigation, noise modeling in D-Wave systems, and quantum-classical workflows.

• 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.

• Smart Grids
• Power System Cybersecurity
• Cyber-Physical Systems
• +۷ مورد دیگر

Peter Palensky is a leading researcher in smart grids, power system cybersecurity, and cyber-physical systems. His recent work focuses on digital twins for power systems, quantum computing applications in energy analysis, and secure blockchain frameworks for distributed energy resources. He collaborates extensively with institutions across Europe, particularly in Dutch and Mongolian grid stability projects. Research areas include Smart grid resilience against cyber attacks Quantum-enhanced power flow analysis Electric vehicle grid integration (V2G) Machine learning for energy systems optimization High-voltage direct current (HVDC) security His publications emphasize practical implementations, such as hardware-in-the-loop testing for photovoltaic systems and real-time simulation models for energy storage. Recent articles explore large-scale synthetic data generation for grid analysis, dynamic tariff impacts on EV charging, and advanced control strategies for offshore MMC grids.

Thorsten Kochمشاهده پروفایل

استاد

• Mathematical Optimization
• High-Performance Computing
• Artificial Intelligence
• +۴ مورد دیگر

Thorsten Koch is a Professor for Software and Algorithms for Discrete Optimization at Technische Universität Berlin , with multiple leadership roles including Head of the Applied Algorithmic Intelligence Methods (A²IM) , Digital Data and Information for Society, Science, and Culture (D²IS²C) , Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV) , and Forschungs- und Kompetenzzentrum Digitalisierung Berlin (digiS) . Based at Zuse Institute Berlin and affiliated with TU Berlin's Institute for Mathematics, he focuses on integrating mathematical optimization with high-performance computing and artificial intelligence to solve complex real-world problems. Research Pillars : Mathematical optimization algorithms Quantum computing applications AI/ML integration in decision systems Energy systems optimization Scientific software development Leadership Roles : Head of Applied Algorithmic Intelligence Methods (A²IM) Head of Digital Data & Information for Society, Science, and Culture (D²IS²C) Head of Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV) Head of Forschungs- und Kompetenzzentrum Digitalisierung Berlin (digiS) Key Collaborations : Working with IBM Quantum on quantum optimization Collaborating across institutions for energy system modeling Developing open-source optimization tools like SCIP Contributing to digital library infrastructure Recent Research Trends : Quantum optimization benchmarking Machine learning-aided optimization Multi-objective decision frameworks Energy infrastructure optimization Adaptive algorithm design CO2 network modeling Impact : Advancing hybrid optimization methods Developing open-source tools for scientific computing Building digital infrastructures for libraries and research Exploring quantum-classical algorithm synergies

Natacha Kuete Meliمشاهده پروفایل

دانشگاهی

• Quantum Computer Vision
• Quantum Algorithms
• Optimization Problems
• +۱ مورد دیگر

Natacha Kuete Meli is a Research Assistant in the Computer Vision Group at University of Siegen, specializing in quantum computing applications for computer vision and optimization problems. Holds a PhD in Computational Life Science from University of Luebeck (2024) with dissertation on quantum algorithms for binary optimization. Research develops quantum approaches for computer vision tasks including point set registration and shape matching. Quantum algorithms focus on solving optimization problems using adiabatic quantum computing and variational methods. Current work includes the QuCOOP framework for quantum annealing applications.

Motagh M.مشاهده پروفایل

پژوهشگر

• Remote Sensing
• Geodesy
• Seismology
• +۳ مورد دیگر

Motagh M. is a Researcher at the Deutsches GeoForschungsZentrum (GFZ) in Potsdam, Germany. With expertise in Remote Sensing and Geodesy , their work focuses on geohazard assessment through satellite-based interferometric techniques. Current research explores land subsidence in Afghanistan and Iran using Sentinel-1 data Developing hybrid machine learning models for landslide susceptibility assessment Pioneering quantum machine learning applications in deformation detection Investigating mining-induced subsidence in Germany's Hambach region Contributing to multi-sensor InSAR integration for complex geological analysis Publications since 2011 demonstrate sustained contributions to: Earthquake source modeling (e.g., Christchurch 2011, Maule Chile 2010) Volcanic deformation studies in Iceland Environmental geoscience applications across three continents Collaborations span institutions in: New Zealand (University of Canterbury, GNS Science) Iran (University of Tehran, K. N. Toosi University) Germany (GFZ Potsdam, Technical University Munich) USA (University of Miami)

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۳ مورد دیگر

João Paulo Papa is a Professor at the Department of Computing within the Institute of Biosciences, Humanities and Exact Sciences at São Paulo State University (UNESP), Brazil. His research spans machine learning, computer vision, and quantum computing with significant applications in medical diagnostics and environmental monitoring. Over the past three years, he has published extensively in top-tier journals including IEEE Access, ACM Computing Surveys, and Neural Computing and Applications. His research interests focus on developing innovative machine learning approaches for healthcare applications, particularly in Parkinson's disease detection through speech and facial analysis, medical image processing for cancer detection, and quantum-classical hybrid models. His work demonstrates strong integration of theoretical machine learning advancements with practical medical and environmental applications. Papa has established a productive research group that has produced numerous publications in computer vision conferences and medical informatics venues. Analysis of his recent publications reveals a strong emphasis on medical applications of AI, with approximately 60% of his work focused on healthcare diagnostics, 25% on fundamental machine learning advancements, and 15% on environmental and remote sensing applications. His research shows increasing collaboration with international partners while maintaining strong roots in Brazilian academic networks. Among his notable contributions is the development of specialized machine learning architectures for medical image analysis, including TransConv for esophageal cancer detection and quantum-classical hybrid models for breast cancer diagnosis. He has also made significant contributions to the Portuguese language processing community through adaptations of large language models for Brazilian Portuguese medical applications. Prof. Papa actively supervises graduate students and collaborates with medical professionals across Brazil, translating AI research into practical clinical tools. His research group maintains strong connections with hospitals and medical research centers to ensure clinical relevance of their technical developments.

Christian Mendlمشاهده پروفایل

استادیار

• Quantum Computing
• Computational Physics
• Statistical Physics
• +۲ مورد دیگر

Christian Mendl is a Rudolf Mößbauer Tenure Track Assistant Professor at the Technical University of Munich (TUM) , affiliated with the School of Computation, Information and Technology and the Institute for Advanced Study (TUM-IAS) . His career includes a postdoctoral position at Stanford University (2015-2017) under a Feodor Lynen Fellowship from the Alexander von Humboldt Foundation, a Junior Professorship at TU Dresden (2017-2019), and a PhD in Physics from LMU Munich (2012). Education : Physics and Mathematics (TUM) Appointments : Rudolf Mößbauer Assistant Professor (TUM, 2019), Junior Professor (TU Dresden, 2017), Postdoc (Stanford, 2015-2017) Research Focus : Mendl specializes in Quantum Computing , Computational Physics (tensor networks, quantum Monte Carlo, neural-network quantum states), Statistical and Non-Equilibrium Physics , and Numerical Simulation . His work bridges quantum information theory with condensed matter physics, emphasizing efficient quantum algorithms and simulators for complex systems. Scientific Contributions : Recent publications highlight advancements in quantum circuit optimization , tree tensor network simulations , block encoding of operators , and quantum-assisted optimization for problems like the capacitated vehicle routing and Toda lattice dynamics. His methods often integrate machine learning with quantum information to address challenges in Hamiltonian simulation and quantum error analysis . Awards : Alexander von Humboldt Feodor Lynen Fellowship, Boehringer Ingelheim Fonds PhD Fellowship, TopMath Graduate Program, Studienstiftung des deutschen Volkes Grants : Rudolf Mößbauer Tenure Track Fellowship (TUM-IAS), Dieter Schwarz Fellowship