Find Academic Supervisors

• Numerical Solution of Partial Differential Equations
• Spectral Element Methods
• Reduced Basis Methods
• +3 more

Einar Malvin Rønquist is a Professor and Head of the Department of Mathematical Sciences at NTNU since August 2013. He holds a MSc from NTNU (1980) and a PhD from MIT (1988). His research focuses on numerical solutions of partial differential equations, spectral element methods, reduced basis methods, and computational fluid dynamics. He has been a leader in several research initiatives, including the Computational Science and Visualization program at NTNU (2003–2011). Rønquist is a member of prestigious academies: NTVA (since 2005) and DNKVS (since 2010). He has supervised 8 PhD students and 25 MSc students. His work spans computational science, with notable contributions to parametric modeling, parallel computing, and fluid dynamics simulation. His administrative roles include Vice President of R&D at Nektonics, Inc. (1991–1999) and Deputy Head of NTNU’s Department of Mathematical Sciences (Fall 2012). His publications highlight advancements in numerical methods for PDEs, including spectral element techniques, reduced basis approaches, and high-order approximations for complex geometries.

• Smart Wireless Systems
• Internet of Things (IoT)
• Information Security
• +8 more

Mohammad Derawi is a Professor in the Department of Electronic Systems at the Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), Gjøvik campus. He leads the Smart Wireless Systems (SWS) research group and serves as the scientific leader of the IoT Lab at NTNU Gjøvik. Educational Background: PhD in Information Security from NISLab (Norway) and CASED (Germany) BSc and MSc in Informatics from DTU (Denmark) His research interests span smart wireless systems, Internet of Things (IoT), information security with a focus on biometric authentication, digital electronics, applied machine learning for activity recognition, and e-learning technologies. His work integrates cybersecurity, embedded systems, and data science to develop secure and intelligent IoT solutions for real-world applications. The recent publications highlight a strong trend in mmWave-based sensing for unmanned aerial systems, RF fingerprinting for secure identification, IoT security frameworks, and machine learning applications in education and human resource analytics. His research bridges theoretical innovation with practical implementation, particularly in smart cities, healthcare, and transportation. Scientific Awards and Recognition: Invitation to the Crown Prince and Princess's 50th birthday celebration, 2023 Study Quality Award, NTNU, 2017 Norway’s Youngest Professor Award, 2016 Denmark’s youngest M.Sc. engineering award, 2009 IEEE Commendation for Young Professionals Volunteer, 2011 Multiple best paper awards from IEEE, ACM, and Springer Mohammad Derawi has been involved in several funded research and development projects, including IoT Safetraffic (RFF Inland), Ambulance Drone (NTNU Vice-Rector), Wireless ECG (Innovation Norway), biometric handgun security (RFF Innlandet), and the EU Framework 7 TURBINE project. He mentors students and collaborates with international researchers, contributing significantly to both academic and applied domains. His leadership in the SWS group and IoT Lab fosters innovation in wireless and secure embedded systems. He is actively engaged in laboratory and team-based research, particularly through the Smart Wireless Systems group and the IoT Lab, focusing on developing secure, intelligent, and scalable solutions for next-generation wireless applications.

Fredrik ManneView profile

Professor

• Parallel Computing
• Distributed Computing
• Graph Algorithms
• +3 more

Fredrik Manne is a Professor at the Department of Informatics, University of Bergen, Norway. His work focuses on parallel and distributed computing, particularly in combinatorial scientific computing and self-stabilizing algorithms. He has contributed extensively to graph algorithms, including matching, coloring, and clustering, with applications in high-performance computing, numerical optimization, and wireless networks. His research involves designing algorithms for parallel architectures, including multi-core and GPU-based systems. He has co-authored numerous publications on topics such as spanning forests, vertex cover heuristics, and b-matching. His work often integrates theoretical and applied approaches, addressing challenges in sparse matrix computations and wireless mesh network communication. The recent articles highlight his exploration of graph neural networks for parallel coloring, efficient multithreaded matching algorithms, and GPU-accelerated clustering methods. These studies emphasize scalability, optimization, and real-world applications in computational science.

• Atomic-scale modelling
• Data analysis
• Friction
• +3 more

Are Strandlie is a Professor at the Department of Manufacturing and Civil Engineering , Norwegian University of Science and Technology (NTNU). His research focuses on Atomic-scale modelling , Material appearance , and Material science , with a particular emphasis on composite materials and 3D printing technologies. He is affiliated with the ApPEARS (Appearance Printing - European Advanced Research School), a European network driving innovative training programs in printing technologies. His work also includes the AQMA project (2021–2024) on air quality monitoring and SFI Manufacturing , a cross-disciplinary center for competitive high-value manufacturing in Norway.

• Computer Architecture
• High-Performance Computing
• Energy Efficiency
• +3 more

Lasse Natvig is a Professor at the Department of Computer Technology and Informatics, Norwegian University of Science and Technology (NTNU), specializing in computer architecture and high-performance computing systems. His research focuses on vector processing optimizations (including AVX-512 extensions), energy-efficient many-core processor design, and simulation frameworks for micromobility planning. Key contributions include vectorized algorithms for compressed datasets, data reuse transformations, and performance analysis of hardware accelerators like Xeon Phi. Recent publications demonstrate consistent innovation in leveraging vectorization for performance and energy gains across domains—from molecular dynamics to urban transportation simulation—with strong emphasis on rigorous experimental validation and open-source tool development. He actively collaborates on projects including the current "Climbing Mont Blanc (CMB)" initiative and completed efforts like "ParVec" and "Energy Efficient Multi-core Processors (EEMP)", reflecting sustained engagement in funded research on advanced computing techniques.

• Power Electronics
• Electric Drives
• Microgrid
• +6 more

Professor Shujun Zhang is a faculty member at the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences (HVL). He specializes in power electronics, electric drives, and microgrid systems with applications in maritime engineering, renewable energy, and battery technology. His work bridges academia and industry through collaborative projects and technical consultancy. Power Electronics Electric Drives Microgrid for Maritime Systems Renewable Energy Integration Digital Real-Time Control Systems His research focuses on developing state-of-the-art power conversion systems, including modular multilevel converters and hybrid propulsion systems. He leads projects with industry partners like Siemens Bergen and Norwegian Electric Systems AS, emphasizing hardware-in-the-loop testing and embedded programming. Recent publications highlight applications of power electronics in electric ferry charging systems, DC/AC converter design, and stability analysis of hybrid microgrids. His lab features advanced equipment: Modular Multilevel Converter Prototypes Typhoon HIL 604 DSP/FPGA Boards Permanent Magnet and Induction Motors Basic Line HIL Machines

• Structural Engineering
• Concrete Structures
• Structural Reliability
• +7 more

Max Hendriks is Professor in the Department of Structural Engineering at the Norwegian University of Science and Technology (NTNU), specializing in advanced structural analysis and assessment methodologies for concrete infrastructure. His work integrates computational modeling with experimental validation to address critical challenges in structural safety and durability. His research focuses on: Structural reliability assessment using proof load testing and monitoring data Acoustic emission techniques for concrete damage detection Nonlinear finite element analysis of cracking and shear behavior Durability mechanisms including alkali-silica reaction and corrosion Numerical modeling of structural response to extreme events Analysis of his 2021-2025 publications reveals a methodological evolution toward integrated experimental-computational frameworks, particularly in acoustic emission waveform modeling for fracture characterization and reliability-based assessment of aging infrastructure. His work consistently bridges theoretical advances with practical engineering applications, emphasizing probabilistic approaches for structural safety evaluation under complex degradation scenarios.

• Applied Mathematics
• Numerical Analysis
• Parallel Computing
• +7 more

André Brodtkorb is a Professor and Head of the Department of Information Technology at Oslo Metropolitan University. His research spans applied mathematics, numerical analysis, and computational science, focusing on physics simulations and GPU computing. He advocates for open and reproducible research and is actively involved in education and societal engagement through the Academy of Young Researchers (2024-2028). Research Interests: His work integrates applied mathematics and computer science to develop high-performance simulations for environmental phenomena, including ocean currents, volcanic ash dispersion, and coastal flooding. He specializes in GPU-accelerated parallel computing, finite-volume methods, and Python-based scientific programming. Publication Trends: Recent articles highlight advancements in GPU computing efficiency, ocean modeling, and inverse ash transport modeling for volcanic plume forecasting. His research bridges computational methods with real-world environmental challenges. Scientific Awards: Member of the Academy of Young Researchers (2024-2028) Contact Information: Office: Pilestredet 35, 0166 Oslo Phone: +47 456 19 070 (Mobile), +47 672 35 924 (Office) Email: andre.brodtkorb@oslomet.no

• Gas separation membranes
• Membrane contactors
• Separation process design
• +4 more

Liyuan Deng is a Professor in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU) , specializing in advanced membrane technologies for gas separation and electrochemical processes. Their work bridges fundamental materials science with industrial-scale carbon capture and battery innovation. Academic appointments at NTNU since at least 2019 Coordinated >10 major EU/Norwegian Research Council projects Active in CO2 capture , membrane contactors , and electrodialysis systems Research Focus: Develops cutting-edge membranes for multiple applications: CO2 separation from flue gas, pre-combustion syngas, and direct air capture Hybrid systems combining ionic liquids , nanocellulose , and metal-organic frameworks Next-generation Li-S battery separators for enhanced electrochemical performance Process optimization for subsea natural gas dehydration and metal recovery in electrodialysis Publications Trends: Recent works (2023-2025) emphasize: Microstructure engineering in polymer membranes (e.g., Nafion, Pebax) Nanocomposite integration (graphene oxide, ZIF-67, MOFs) Standardization challenges in CO2 capture membrane testing Electrodialysis applications for precious metal recovery Multi-site interaction membranes for selective separations Computational modeling of gas transport and solvent regeneration Grants & Collaborations: Leads projects funded by: Research Council of Norway (Nano-2021, CLIMIT, BIA programs) EU Horizon2020 ( NanoMEMC2 ) M-ERA.NET ( MOGLiS ) Technology Converge ( 3S Battery )

Olivier LartillotView profile

Researcher

• Computational Music Analysis
• Artificial Intelligence
• Music Information Retrieval
• +6 more

Olivier Lartillot is a Researcher at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion at the University of Oslo. His work focuses on computational music and sound analysis, artificial intelligence, and music information retrieval. He developed the MIRtoolbox and MiningSuite frameworks, tools widely used in music analysis. Previously, he held roles including Academy of Finland Research Fellow (2009–2014) and postdoctoral positions at the Finnish Centre of Excellence in Interdisciplinary Music Research. Education: PhD in Music and Acoustics from IRCAM/UPMC Paris (2000–2004); Master’s in Musicology from Sorbonne University (1996–1999). Research Projects: MIRAGE (AI-based music analysis), SoundTracer (Norwegian folk music transcription), HIGH-M (music and health interaction). His research interests span computational models of music structure, cross-cultural music analysis, and applications in music therapy and digital heritage. He has authored over 80 articles (3,000+ citations) and contributed to initiatives like the Norwegian Folk Music Archive digitization. He is a member of the editorial board for Transactions of the International Society for Music Information Retrieval and an expert evaluator for EU Horizon 2020 programs. His work bridges technical innovation with musicological theory, addressing challenges in automated transcription, segmentation, and expressive performance analysis.

• Theoretical Physics
• Quantum Field Theory
• Early Universe Cosmology
• +4 more

Anders Tranberg is a Professor of Theoretical Physics at the University of Stavanger, affiliated with the Faculty of Science and Technology and the Department of Mathematics and Physics. His research focuses on classical and quantum field phenomena in particle physics and cosmology, including baryogenesis, inflation, preheating, topological defects, and out-of-equilibrium field theory methods. He employs high-performance computing alongside analytical techniques and model-building. Education and Career: Master of Science, University of Copenhagen (Niels Bohr Institute), 2000. Ph.D. in Theoretical Physics, University of Amsterdam, 2000–2004. Postdoctoral Fellowships at University of Sussex, Cambridge, Oulu, and Helsinki (2004–2010). Assistant Professor, University of Copenhagen (2010–2012). Full Professor, University of Stavanger (2013–present). Research Interests: Tranberg’s work emphasizes real-time quantum field theory methods (e.g., Lefschetz thimbles, 2PI formalisms) and quantum effects in the early universe. Collaborations include projects on inflationary dynamics with NTNU and phase transition simulations with the University of Nottingham. His research also explores gravitational wave signatures from cosmological phase transitions and topological defects. Key Contributions: His publications span topics like quantum-corrected Q-ball dynamics, bubble nucleation simulations, and stochastic inflation models. He actively participates in conferences and public lectures, including presentations on dark matter, gravitational waves, and quantum mechanics at the Wonderful World Festival.

Gianfranco NencioniView profile

Associate Professor

• Mathematical Modelling and Optimization
• Dependability
• Energy-Efficiency
• +7 more

Gianfranco Nencioni is an Associate Professor at the Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, University of Stavanger, Norway, since 2018. He leads the Computer Networks (ComNet) research group and serves as principal investigator for the Norwegian Research Council-funded project 5G-MODaNeI. Ph.D., Information Engineering, University of Pisa (2009–2011) M.S., Telecommunication Engineering, University of Pisa (2006–2008) Postdoctoral fellow, NTNU (2015–2018) and University of Pisa (2012–2015) His research focuses on mathematical modeling and high-performance optimization of resource allocation in wired/wireless networks, with emphasis on dependability , energy-efficiency , and emerging technologies like SDN, NFV, and 5G. Recent work explores AI-driven orchestration for smart cities and security-dependent availability models in 5G-MEC systems. Article trends highlight his contributions to multi-objective optimization (energy, cost, availability), deep reinforcement learning for network slicing, and security-dependability co-design in edge computing. Key projects include 5G-MODaNeI and development of hybrid testbeds for MEC applications.

Mahdi KioumarsiView profile

Associate Professor

• Concrete Technology
• Structural Engineering
• Corrosion Engineering
• +6 more

Mahdi Kioumarsi is an Associate Professor at Oslo Metropolitan University (OsloMet) and Østfold University College, specializing in structural engineering with a focus on advanced concrete technology and deteriorated concrete structures. He holds a Ph.D. from NTNU (2015) and an M.Sc. from Amirkabir University of Technology (2009). His research integrates experimental work with finite element modeling, addressing corrosion effects, structural performance, and sustainable construction materials. Education: Ph.D. in Structural Engineering, NTNU (2015) M.Sc. in Structural and Earthquake Engineering, Amirkabir University (2009) Research Interests: His work spans from material-level studies to full-scale structural analysis, emphasizing corrosion mitigation, nonlinear behavior, and sustainable materials. Key focus areas include: Corrosion-resistant concrete formulations Seismic behavior of historical structures Lifecycle assessment of construction materials Machine learning applications in structural analysis Recent Research Trends: Most recent publications emphasize sustainable materials innovation (e.g., ultra-high performance concrete with waste additives), corrosion impact analysis, and AI-driven predictive models for material properties. He co-edited the 2025 conference proceedings on net-zero built environments. Labs/Teams: Active in research groups like the Building and Environment Research Group and Structural Engineering Research Group (SERG). Collaborates internationally on projects like HYPERION (resilience of historic areas).

Thanh LeView profile

Research Fellow

• Distributed Systems
• Machine Learning
• Deep Learning
• +4 more

Thanh Le is a Doctoral Research Fellow at the Department of Informatics, University of Oslo, affiliated with the Networks and Distributed Systems research group. Their work spans multiple domains including machine learning, edge/cloud computing, and embedded systems. Research Interests: The fields_of_interest include Distributed Systems, Deep Learning, Embedded Systems, and Edge Computing. Their research focuses on optimizing AI models for edge environments, applying deep learning to medical diagnostics, and developing smart systems for energy and healthcare applications. Publications: Thanh Le's articles demonstrate expertise in edge computing optimization, medical imaging analysis, and IoT-based solutions. The work from 2022-2024 shows a trend of integrating deep learning with spectrum management, renewable energy systems, and healthcare logistics. Laboratory Affiliation: Member of the Networks and Distributed Systems (ND) research group at the University of Oslo.

• Cloud Computing
• Cybersecurity
• Software Engineering
• +6 more

Kyrre Begnum is an Associate Professor at OsloMet – Oslo Metropolitan University, affiliated with the Faculty of Technology, Art and Design through the Department of Computer Science. His research spans cloud computing, cybersecurity, and software engineering, with a focus on virtual machine management, intrusion detection, and autonomic systems. Cloud Computing Cybersecurity Software Engineering Machine Learning Data Center Management His recent publications emphasize adversarial machine learning in security, affinity-aware scheduling for cloud efficiency, and network optimization in virtualized environments. While no formal awards are documented in the provided texts, his work demonstrates technical innovation in distributed systems and educational applications of virtualization technology.