High Performance Computing Professors and Supervisors in Denmark | ApplySharif

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Jacob ØstergaardView profile

Professor

Technical University of DenmarkDenmark

Jacob Østergaard is a Professor and Head of the Division for Power and Energy Systems at DTU Wind and Energy Systems, Technical University of Denmark. His research focuses on renewable energy systems, offshore wind power hubs, and quantum computing applications in energy systems. He leads initiatives like EnergyLab Nordhavn and PowerLabDK, emphasizing collaboration between academia and industry. Education: MSc in Electrical Engineering from DTU (1989–1995). External positions include roles at Research Institute of the Danish Electric Utilities and Ørsted (now SK Energy). Research Interests: Power system stability, flexibility markets, offshore wind energy, quantum computing in energy systems, Power-to-X, and energy storage. He advocates for integrated, market-based energy systems to achieve the green transition. Publications highlight quantum computing for grid optimization, offshore energy hubs, and Denmark’s energy island strategy. Recent work emphasizes scientific advice for energy policy and green hydrogen production. Awards: A. Angelo’s Prize (1996), AEG Electron Prize (2007), Danish Design Award (2019), and EU RESponsible Island Prize (2020). Advising and Grants: Supervises PhD students in grid integration and control. Active in projects like OEH (Offshore Energy Hubs) and BOSS (Battery Energy Storage System). His work drives Denmark’s energy policy through roles on Energinet’s board and the Danish Energy Commission. Labs/Teams: Leads PowerLabDK and EnergyLab Nordhavn, experimental facilities for smart grid and energy system research.

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Jørgen Arendt JensenView profile

Professor

Technical University of DenmarkDenmark

Jørgen Arendt Jensen is a Professor of Biomedical Signal Processing at the Technical University of Denmark (DTU), with dual affiliations in the Department of Health Technology and the Department of Electrical Engineering (DTU Elektro). He leads the Center for Fast Ultrasound Imaging (CFU), a collaborative initiative involving DTU, BK Medical, Rigshospitalet, and DTU Nanotech. His research focuses on advanced medical ultrasound technologies, including synthetic aperture imaging, vector flow imaging, and ultrasound simulation, aiming to improve clinical image acquisition efficiency and accuracy. He teaches medical imaging courses and co-initiated the joint biomedical engineering program between DTU and the University of Copenhagen. Jensen’s work contributes to UN Sustainable Development Goals related to health and innovation. His research interests span algorithm development for fast ultrasound imaging, blood velocity characterization, and simulation of ultrasound systems. He supervises multiple PhD students and collaborates on projects involving transducer design, real-time imaging systems, and microvascular pathology analysis. Recent publications emphasize advancements in super-resolution ultrasound imaging, transducer optimization, and pressure gradient estimation. His lab, CFU, develops cutting-edge imaging solutions for clinical applications. Jensen’s contributions include patents on ultrasound imaging techniques and collaborative ventures to enhance diagnostic capabilities through interdisciplinary engineering.

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Torsten BerningView profile

Associate Professor

Aalborg UniversityDenmark

Torsten Berning serves as Associate Professor at AAU Energy within The Faculty of Engineering and Science at Aalborg University, Denmark. His research focuses on thermal engineering systems, hydrogen production technologies, and electro-fuels development, with significant contributions to fuel cell and electrolyzer innovation. His primary research domains include fuel cell engineering, electrolyzer technology, and computational fluid dynamics applied to energy systems. He investigates water management in proton exchange membrane fuel cells, heat and mass transfer in electrolysis cells, and efficiency optimization of hydrogen production systems. His methodology combines experimental validation with advanced CFD modeling to address durability and performance challenges in electrochemical energy conversion. Recent publications (2024-2025) demonstrate concentrated research on alkaline electrolysis systems and thermal management solutions. Key advancements include reducing gas crossover in electrolyzers, optimizing indirect evaporative coolers, and designing proton exchange membrane electrolyzers. His work leverages computational modeling to enhance efficiency and scalability of hydrogen production technologies while addressing multiphase flow and heat transfer complexities. Professor Berning actively supervises PhD candidates: H. D. Miller on Degradation Modeling and Lifetime Prediction of Electrolyzers D. L. Martinho on Computational Fluid Dynamics of Alkaline Electrolysis Cells W. Liu on Water Transport in Proton Exchange Membrane Fuel Cells His research is funded by multiple grants including EUDP's 'Boosting Economic Electrolyzer Stack Technology 2' (2022-2025) and the Danish Energy Agency's 'Degradation Modeling and Lifetime Prediction of Electrolyzers' project (2024-2027). He collaborates within AAU Energy's research ecosystem on hydrogen production systems and thermal management technologies, contributing to projects like the Adiabatic Cooling Systems for Decentralised Ventilation and advancing electrolyzer stack technology through industry partnerships.

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Erik Bjørnager DamView profile

Professor

University of CopenhagenDenmark

Erik Bjørnager Dam is a Professor in the Machine Learning section at the Department of Computer Science, University of Copenhagen (UCPH). His research spans theoretical foundations of machine learning to practical applications in medical data analysis, sustainability, and materials science. His key research interests include: Small-scale and resource-efficient deep learning Medical image analysis and segmentation Sustainable and environmentally conscious AI development Graph neural networks for materials science Resource-constrained AI systems Professor Dam's recent publications demonstrate a strong focus on making AI more accessible and sustainable while maintaining high performance standards. His work on 'Performance Per Resource Unit' metrics addresses critical challenges in deploying AI in resource-limited environments, particularly in healthcare applications. His research bridges theoretical machine learning with practical implementations across multiple domains. His notable professional activities include: Co-founding Cerebriu A/S (since 2018) Co-founding Biomediq A/S (since 2008) Delivering lectures on AI's role in green transition (April 24, 2023) Media contributions on deep learning applications in plant research (September 13, 2018) With 74 documented research outputs, Professor Dam maintains an active research profile with significant contributions in 2023-2025 across medical imaging, sustainable AI, and materials science applications.

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Rasmus PaghView profile

Professor

University of CopenhagenDenmark

Rasmus Pagh is a Professor at the Department of Computer Science, University of Copenhagen, specializing in algorithms and complexity. His career includes a 2002 PhD from Aarhus University under Peter Bro Miltersen and a tenure at IT University of Copenhagen until 2020. He leads theoretical research with practical applications in big data, databases, and modern computer architecture parallelism. His research interests span algorithms, data structures, and privacy-preserving computing. Recent work includes the ERC-funded project on Scalable Similarity Search and contributions to the BARC center for basic algorithms research. He has collaborated with Google Research (2019-2020) and focuses on theoretical foundations with real-world impact. Key research trends in his 2023-2024 publications include privacy-preserving data analysis probabilistic data structures distributed secure computation noise-robust coding hashing efficiency continual privacy mechanisms Scientific recognition includes 2024 ACM Fellowship ERC grant leadership multiple top-tier conference publications

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Jens Honore WaltherView profile

Professor

Technical University of DenmarkDenmark

Jens Honore Walther is a Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU). His research focuses on fluid mechanics, coastal and maritime engineering, and computational fluid dynamics (CFD). He leads projects on wave energy converters, multiphase flow systems, and thermal energy applications. His work contributes to sustainable development goals related to clean energy and climate action. External Roles: Research associate at ETH Zurich (2003–present) Postdoctoral fellow at ETH Zurich (2000–2003) Project manager at Danish Maritime Institute (1996–1997) Research scientist at Danish Meteorological Institute (1994–1996) Research Interests: Walther’s expertise spans CFD modeling, granular flow dynamics, and nanofluidics. His recent projects include optimizing wave energy converters, analyzing gap resonances in marine structures, and developing multiphase ejector geometries for heat pumps. His work integrates high-performance computing and experimental validation to address challenges in marine engineering and energy systems. Advising & Projects: He supervises PhD students in areas such as elite sport aerodynamics, gas lubrication, and alternative fuel combustion. Notable projects include: Elite sport aerodynamics (2024–2026) Alternative fuel injection in marine engines (2023–2026) Multi-physical gas bearing modeling (2024–2027) Labs & Collaborations: Walther collaborates with institutions like ETH Zurich and engages in experimental facilities at DTU. His group focuses on advanced CFD simulations and fluid-structure interaction studies.

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Martin AumüllerView profile

Lecturer

IT University of CopenhagenDenmark

Martin Aumüller is a Lecturer in Theoretical Computer Science Algorithms at the IT University of Copenhagen . He serves as Head of Education and Master of Software Design , focusing on algorithm engineering, differential privacy, and similarity search. Research interests include: Algorithm engineering for high-dimensional data Locality-sensitive hashing and nearest neighbor search Privacy-preserving machine learning Fairness in approximate search algorithms Benchmarking and evaluation of similarity search tools Publications trends highlight his work on approximate nearest neighbor search , privacy-preserving techniques , clustering algorithms , and scalable outlier detection in high-dimensional spaces. His recent projects (2024-2025) focus on fairness, differential privacy, and efficient indexing. Grants and projects : DIREC (2020-2025): Digital Research Centre Denmark (Innovation Fund Denmark) DIREC: Bias and Benefit of Approximate Nearest Neighbor Search (2022-2025): Principal Investigator (Innovation Fund Denmark) BARC (2017-2024): Basic Algorithms Research Copenhagen (Villum Fonden) SSS (2014-2019): Scalable Similarity Search (European Commission)

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Yan Kyaw TunView profile

Assistant Professor

Aalborg UniversityDenmark

Yan Kyaw Tun is a Tenure Track Assistant Professor in the Department of Electronic Systems at Aalborg University's Technical Faculty of IT and Design, located in Copenhagen, Denmark. His research lies at the intersection of wireless communications, edge computing, and artificial intelligence, with a strong focus on next-generation networks (5G/6G), UAV-assisted systems, and intelligent resource management. His educational background includes a Ph.D. in Computer Engineering from Kyung Hee University, South Korea, where he was awarded the Best Ph.D. Thesis Award in 2021, and a Bachelor of Engineering in Marine Electrical Systems and Electronic Engineering from Myanmar Maritime University. Dr. Tun's research interests span Edge Computing , Multi-Access Edge Computing (MEC) , Resource Allocation , Unmanned Aerial Vehicles (UAVs) , Reinforcement Learning , Energy Efficiency , and Integrated Sensing and Communication (ISAC) . His work leverages AI and optimization techniques to enhance the performance of wireless networks, particularly in space-air-ground integrated systems and satellite-HAP environments. The recent publications highlight a clear trend toward intelligent and sustainable networking: the integration of STAR-RIS (Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces), Federated Learning for satellite-HAP systems, and AI-driven optimization for UAV trajectories and beamforming. These works are published in high-impact venues such as IEEE Transactions on Mobile Computing and IEEE ICC , showcasing his leadership in cutting-edge communication technologies. His scientific accolades include: IEEE ComSoc Outstanding Young Researcher Award for EMEA Region (2024) Best Ph.D. Thesis Award (2021) Student Best Paper Award at APNOMS 2019 Korea Network Operation and Management Conference Award (2020) Korea Computer Congress 2018 Award Dr. Tun is actively engaged in the academic community as an advisor and grant participant. Though no direct advisees are listed, his involvement in large collaborative projects—evidenced by co-authorship with senior researchers like Prof. Choong Seon Hong—indicates mentorship and team leadership. He has served on the editorial boards of IEEE Internet of Things Journal , IEEE Open Journal of the Communications Society , and IEEE Network , and has secured research support through participation in IEEE-organized workshops and special issues. He is a key organizer of upcoming workshops, including the 'Sustainable AI for Next-Generation Wireless Communications and Networking' at IEEE GLOBECOM 2025 and the 'Digital Twin Networks' workshop at IEEE/CIC International Communications in China 2025, reflecting his role in shaping future research directions in intelligent and green networking.

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Kasper Green LarsenView profile

Professor

Aarhus UniversityDenmark

Kasper Green Larsen is a Professor in the Department of Computer Science at Aarhus University. His research focuses on theoretical computer science, machine learning, algorithms, and data structures. He has made significant contributions to boosting algorithms, PAC learning theory, and computational geometry. His work often bridges algorithm design with complexity theory, addressing challenges in optimization, memory efficiency, and lower bounds analysis. Key research areas include: Algorithmic Learning Theory (e.g., boosting, bagging, and PAC learners) Data Structure Design (e.g., invertible Bloom tables, succinct representations) Computational Complexity (e.g., lower bounds for dynamic and oblivious algorithms) Geometric Algorithms (e.g., hierarchical searching, range queries) Recent publications emphasize foundational advancements in learning theory (e.g., optimal weak-to-strong learning) and data efficiency (e.g., memory-reduced Bloom filters). His work frequently appears in top conferences like IJCAI, ICALP, and SODA, reflecting rigorous theoretical contributions with practical implications.

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Lars DittmannView profile

Professor

Technical University of DenmarkDenmark

Lars Dittmann is a Professor at the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU), leading the Networks Technology and Service Platforms section. His work bridges advanced networking technologies with real-world applications in healthcare and transportation. Academic Role: Professor, Head of Section University: Technical University of Denmark (DTU) Department: Networks Technology and Service Platforms Research Interests: Professor Dittmann specializes in Software-Defined Networking (SDN) , 5G and IoT technologies , and energy-efficient network design , with a focus on applications in telemedicine and transportation systems . His work integrates machine learning for privacy-preserving traffic analysis and explores optical networks for high-bandwidth scenarios. Scientific Contributions: His recent publications emphasize green cellular networks using SDN/NFV/C-RAN, IoT benchmarking for coverage and mobility, and secure edge architectures for railways. Collaborative projects like the Future Patient telerehabilitation program highlight his interdisciplinary impact. Supervision: He supervises PhD candidates such as Radheshyam Singh, focusing on SDN-based IoT security and 5G network optimization. Labs & Projects: Leads initiatives like EXplorative network PLAnnINg and Broadband Trial Integration , addressing challenges in network reliability , emergency communication , and optical data center scaling .

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Joe AlexandersenView profile

Associate Professor

University of Southern DenmarkDenmark

Joe Alexandersen is an Associate Professor in the Department of Mechanical Engineering at the University of Southern Denmark (SDU), affiliated with the Institute of Mechanical and Electrical Engineering. His research spans structural optimization, heat transfer, fluid dynamics, and high-performance computing, with applications in heat sink design, microfluidic devices, and additive manufacturing. Research Interests Topology and shape optimization Conjugate heat transfer Navier-Stokes flow modeling Finite element methods High-performance computing Scientific Awards 2022 Fluids 2020 Best Paper Award 2017 DTU Young Researcher Award 2015 ISSMO/Springer Prize for Young Scientist Key Projects HiHeaT: Topology optimization for high heat flux components (2024–2027) Structural Analysis of Large Modular Vessels (2025–2027)

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Anders IvarssonView profile

Associate Professor

Technical University of DenmarkDenmark

Anders Ivarsson is an Associate Professor at the Department of Civil and Mechanical Engineering , Thermal Energy , at the Technical University of Denmark. His research focuses on combustion physics, chemical thermodynamics, and sustainable engine technologies. Key Affiliations: Technical University of Denmark (2003–present) Academic Background: PhD in Mechanical Engineering (2008), MSc in Mechanical Engineering (2003) Research Interests include combustion diagnostics, optical engine testing, and CFD modeling for emissions reduction. His work addresses greenhouse gas control and alternative fuel integration. Publications highlight trends in spark ignition engines, ammonia combustion, and radiative heat transfer modeling. Recent studies focus on lignin fuels and spectral radiation analysis. Students: R. W. K. Christensen (PhD, main supervisor) M. Terauchi (PhD, main supervisor) F. R. Westlye (PhD, main supervisor) Projects: Currently leads investigations into marine engine emissions, dual-fuel ammonia combustion, and lignin-based fuels. Past work includes radiation modeling in combustion engines and fuel spray dynamics.

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Martin Skovgaard AndersenView profile

Associate Professor

Technical University of DenmarkDenmark

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.

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Klaus Ingemann PedersenView profile

Professor

Aalborg UniversityDenmark

Klaus Ingemann Pedersen is a Professor in the Department of Electronic Systems at the Technical Faculty of IT and Design, Aalborg University, Denmark. His research focuses on wireless communication networks and dependable wireless communications with substantial contributions to 5G-Advanced and 6G technologies. His research interests include: Wireless Communication Networks Dependable Wireless Communications 5G-Advanced Technology 6G Networks Radio System-Level Simulations Ultra-Reliable Low Latency Communication Heterogeneous Networks Professor Pedersen's recent work demonstrates cutting-edge research in next-generation wireless systems, with particular emphasis on deep reinforcement learning applications, link adaptation techniques, and system-level simulations. His publications show consistent high-impact output with 152 total publications spanning from 2012 to 2025, reflecting ongoing productivity and relevance in the rapidly evolving field of wireless communications. His notable achievements include: 5G-prisen (The 5G Award) received on October 23, 2019, shared with E. Mogensen and I.R. Larrad Professor Pedersen has supervised 8 PhD students according to institutional records. His research has practical applications in wireless communication, as evidenced by his involvement in patented technology 'Method and apparatus for wireless communication' which delivers social impact and quality of life improvements. His work shows strong international collaboration across multiple countries as indicated by the research network visualization.

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Zhanhao ZhangView profile

Research Fellow

Technical University of DenmarkDenmark

Zhanhao Zhang is a Postdoc researcher at the Department of Applied Mathematics and Computer Science (DTU Compute) of the Technical University of Denmark, affiliated with the Scientific Computing Center for Energy Resources Engineering. His work centers on developing advanced control strategies for industrial processes with emphasis on sustainability and computational efficiency in energy-intensive sectors. His research specializes in Model Predictive Control (MPC) for time-delay systems, numerical discretization methods, and real-time implementation of cyber-physical control systems. Key application domains include zero-emission industrial processes, cement production blending operations, and oil/gas zero-discharge systems, where he bridges theoretical control algorithms with practical industrial constraints through close industry collaboration. Analysis of his 2022-2024 publications reveals consistent innovation in extending MPC methodologies to handle complex industrial realities like time delays and discontinuities while targeting environmental sustainability. His work demonstrates strong integration of computational mathematics with process engineering to solve critical challenges in carbon-intensive industries. Zhang completed his PhD project "Model Predictive Control for Zero Emission Industrial Processes" (2021-2024) under Professor John Bagterp Jørgensen at DTU, resulting in his doctoral thesis and multiple high-impact publications. This industry-collaborative project focused on developing control frameworks for decarbonizing industrial operations. As an active member of DTU's Scientific Computing Center for Energy Resources Engineering, he contributes to interdisciplinary research that applies high-performance computing and mathematical modeling to optimize energy resource extraction, processing, and utilization while reducing environmental impact.

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