Find Academic Supervisors

• Computational Chemistry
• Molecular Simulation
• Path Sampling Methods
• +3 more

Professor Titus Sebastiaan van Erp is affiliated with the Department of Chemistry at the Norwegian University of Science and Technology (NTNU), where he has worked since 2016. His research focuses on advancing molecular simulation techniques to study complex biological and industrial processes without approximations, particularly through path sampling methods for rare events. 2016 – Present: Professor, NTNU 2012 – 2016: Associate Professor, NTNU 2006: Centre-of-Excellence Fellow, Leuven 2004: Marie Curie Fellow His research develops innovative methodologies like RETIS and REPPTIS to enhance simulation accuracy and expand accessible time/system scales. He has supervised students in DNA denaturation, electron transfer reactions, and protein folding studies. Recent publications analyze NaCl dissociation, ABL-imatinib kinetics, and permeation mechanisms. His work involves Python-based PyRETIS software development and collaborations across computational chemistry, biophysics, and materials science. 2025: NaCl Dissociation via Predictive Power Path Sampling 2025: RETIS/REPPTIS for Biomolecular Kinetics 2024: PyRETIS 3 for Boundary-Free Rare Events Scientific recognitions include: Centre-of-Excellence Fellowship (2006) Marie Curie Fellowship (2004) He has advised multiple students in masters theses on molecular simulation, including projects on DNA unwinding, electron transfer, and protein folding. His lab integrates algorithm development with applications in chemical reactions, biomolecular systems, and nanoscale materials.

Daniel RomeroView profile

Professor

• UAV Communications
• Wireless Communications
• Time-Series Analysis
• +5 more

**Daniel Romero** is a **Professor** in the **Department of Information and Communication Technology** at the **University of Agder**, Norway. His research focuses on UAV communications, time-series analysis using machine learning and network science, and decentralized processing for sensor networks. He holds a Ph.D. in Signal Theory and Communications from the University of Vigo (2015), an M.Sc. in Signal Theory (2011), and a Telecommunication Engineering degree (2009). **Education**: Ph.D. in Signal Theory and Communications, University of Vigo (2015) M.Sc. in Signal Theory and Communications, University of Vigo (2011) Telecommunication Engineering, University of Vigo (2009) **Research Interests**: His work spans UAV communication systems (focusing on low-latency, high-reliability networks), time-series analysis for complex systems (using ML and network science), and decentralized computation in sensor networks to improve robustness and hardware efficiency. Recent projects include radio map estimation for mmWave beam alignment, spoofing detection via graph neural networks, and aerial base station placement optimization. **Publications**: Over 30+ peer-reviewed articles in top venues like IEEE Transactions on Wireless Communications and ICC. Recent trends emphasize radio map estimation (2023–2024), UAV-enabled spectrum surveying (2022), and robust D2D communications (2022). **Advising & Grants**: Teaches PhD courses (Statistical Signal Processing, Advanced Optimization) and leads the **Advanced Signal Processing Lab (ASL)**. Collaborates with the **CIEM (Center for Integrated Emergency Management)** on crisis-related communication systems. **Labs/Teams**: Directs the Advanced Signal Processing Lab (ASL.uia.no) and contributes to CIEM, applying ML and signal processing to emergency management challenges.

Tor SkeieView profile

Professor

• High-Performance Networking
• Distributed Systems
• InfiniBand Technology
• +2 more

Tor Skeie is a Professor at the University of Oslo's Department of Informatics, specializing in networks and distributed systems. His research focuses on high-performance networking, InfiniBand technologies, and adaptive routing systems. Current investigations include automated parameter tuning for reservoir simulations, adaptive routing in InfiniBand hardware, and modeling WiFi quality attenuation. His work develops efficient solutions for virtualized HPC environments and cloud computing infrastructures. Recent publications demonstrate innovations in network modeling, adaptive routing algorithms, and performance analysis of distributed systems. Research collaborations span European projects on high-performance networking infrastructures. Leads the Networks and Distributed Systems (ND) research group investigating fault-tolerant routing, network virtualization, and congestion control mechanisms.

Ørjan Grøttem MartinsenView profile

Professor

• Bioimpedance
• Biomedical Physics
• Electronics and Instrumentation
• +3 more

Ørjan Grøttem Martinsen is a Professor of Electronics at the Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo. He also holds a temporary research position at the Medical Technology Business Area of Oslo University Hospital. With over three decades of experience, he has established himself as a leading expert in bioimpedance research and applications. Education: High-voltage engineer degree (1983) Cand. scient. in electronics/measurement technology (1990) Dr. scient. with thesis on skin's electrical properties (1995) Professor Martinsen's research centers on bioimpedance—the passive electrical properties of biological tissues that vary with anatomy and physiology. His work spans diverse applications including medical diagnostics (skin cancer detection), food quality assessment (fresh vs. thawed fish), skin condition monitoring (moisture levels), and stress level evaluation. His research bridges physics, engineering, and medical applications, creating practical diagnostic tools from fundamental electrical principles. He has pioneered methods to characterize tissue properties through impedance measurements, with particular focus on electrodermal activity and skin impedance. His recent publications (2022-2025) demonstrate a strong interdisciplinary approach combining bioimpedance with machine learning, robotics, and advanced signal processing. The work spans from fundamental biophysics (GABA detection, tissue characterization) to practical applications (dental anxiety assessment, ADHD treatment evaluation). Key trends include integration of AI with bioimpedance measurements, development of novel sensor systems, and expansion into new application areas like optogenetics and micro-robotics. Awards and Recognition: IEEE Senior Member (2006) CLABIO Award (2012) Fellow at Institute of Physics (FInstP) (2015) Dr. Honoris Causa, Tallinn University of Technology (2018) UiO Innovation Award (2019) Member of Norwegian Academy of Technical Sciences (2021) Professor Martinsen has served as Editor-in-Chief of the Journal of Electrical Bioimpedance since 2010 and was President of the International Society for Electrical Bioimpedance (2010-2016). His research has attracted significant funding, enabling collaborations across engineering, medical, and biological disciplines. He has supervised numerous students and researchers in the Bioimpedance Group at UiO, fostering a strong research environment that bridges theoretical and applied work. His work is conducted primarily through the Oslo Bioimpedance Group and Sensorama SmartSense research teams, which focus on developing innovative measurement techniques and applications of bioimpedance technology. These groups maintain strong collaborations with medical institutions and industry partners to translate research findings into practical healthcare solutions.

Eirik KeilegavlenView profile

Researcher

• Mathematical Modeling
• Numerical Methods
• Porous Media
• +5 more

Eirik Keilegavlen is a Researcher at the Department of Mathematics, University of Bergen. His primary research focuses on developing mathematical models, numerical methods, and simulation tools for multiphysics processes in porous media, particularly in geothermal energy, CO 2 storage, and subsurface energy systems. He leads the development of the open-source software PorePy, designed for simulating processes in fractured porous media. His work emphasizes coupled problems involving fluid flow, heat transfer, and mechanical deformation. Key research interests include: Mathematical modeling of coupled thermal-hydro-mechanical processes Numerical discretization methods for fractured media Development of open-source simulation tools Applications in geothermal energy extraction and carbon sequestration Recent publications highlight advancements in: Uncertainty quantification for CO 2 leakage Viscous fingering in fractured reservoirs Automated solver selection for multiphysics systems Collaborations involve interdisciplinary teams addressing challenges in geothermal reservoir stimulation, fault mechanics, and high-performance computing. His work bridges theoretical developments with practical applications in energy and environmental systems.

Juergen PfingstnerView profile

Researcher

• Particle Accelerator Technology
• Free Electron Lasers
• CLIC (Compact Linear Collider)
• +5 more

Juergen Pfingstner is a Post-doctoral Fellow at the University of Oslo (since 2015) within the Department of Physics. His research focuses on advanced accelerator technologies, particularly at CERN's CLIC Test Facility (ATF2). Key areas include emittance preservation, ground motion mitigation, and free-electron laser (FEL) design. He holds a PhD from the Vienna University of Technology (2013) and a Master's in Electrical Engineering from Graz University of Technology (2008), specializing in control engineering and electromagnetic field computation. His academic journey includes a postdoctoral stint at CERN (2012–2014), where he investigated ground motion effects on CLIC performance. Collaborations span institutions like KEK (ATF2 facility) and the X-band FEL collaboration. Research interests bridge particle accelerator physics, control systems, and high-frequency radiation technologies. Publications emphasize CLIC final focus systems, wakefield suppression, and plasma wakefield acceleration. His work addresses both theoretical and experimental challenges in next-generation collider design, including THz radiation facilities and feedback control methodologies.

Bodil HolstView profile

Professor

• Surface Science
• Scientific Instrumentation
• 2D Materials
• +3 more

Bodil Holst is a Professor at the University of Bergen's Department of Physics and Technology , specializing in surface science and scientific instrumentation development . Her research spans 2D materials , helium atom scattering , and archaeometry applications. She leads projects like Nanometer-Resolution Matter-Wave Lithography (FET-Open), 2D Material Properties (NFR FRIPRO), and Wind Turbine Erosion Prevention (Equinor). Her Nanophysics Group has produced groundbreaking work on graphene's temperature-dependent rigidity and icephobic surfaces . First neutral helium microscope images (2008) Recorded bending rigidity of 2D materials (2018-2021) Developed solid-state conversion techniques for sapphire (2017-2021) Her teaching innovations in classical mechanics explore retrieval practice and digital learning structure , documented in Physics Education and Physical Review Physics Education Research .

• High-performance Computing
• Energy-efficient Computing
• Machine Learning
• +3 more

Dr. Ngoc Nha Vi Tran is an Associate Professor of Computer Science at UiT The Arctic University of Norway. She holds a PhD from UiT and was a visiting scholar at Rutgers University, USA. Her research focuses on high-performance and energy-efficient computing, machine learning, and bioinformatics. She is a member of the NORA.startup Steering Group and leads the Arctic Green Computing Group. Education: PhD in Computer Science (UiT), M.Sc. in Software Engineering via Erasmus Mundus (Blekinge Institute of Technology, Sweden & Technical University of Kaiserslautern, Germany). Research interests include energy-efficient algorithms, bioinformatics tools (e.g., vCOMBAT), and applications of machine learning in healthcare and robotics. She teaches courses such as INF-2200 Computer Architecture, INF-2900 Software Engineering, and INF-2202 Concurrent Programming. Her work spans computational models for antibiotic target-binding, runtime energy optimization (REOH framework), and power models for embedded systems (RTHpower/ICE). She contributed to the EXCESS project on energy-efficient computing systems. Labs/Teams: Arctic Green Computing Group, EXCESS consortium.

• Computational Neuroscience
• Neural Network Simulation
• Large-Scale Simulation
• +3 more

Hans Ekkehard Plesser is a Professor at the Department of Data Science, Norwegian University of Life Sciences (NMBU). His work focuses on computational neuroscience, large-scale neural network simulations, and advancing reproducibility in computational research. He is a key contributor to the NEST simulator, a widely used open-source tool for modeling spiking neural networks. His research spans theoretical and applied aspects of neuroscience, including neuron-astrocyte interactions, connectivity patterns in neural networks, and high-performance computing strategies for brain-scale simulations. He emphasizes reproducible computational practices through initiatives like the ReScience project, promoting transparency and validation in scientific software. His publications highlight advancements in simulation methodologies, algorithm optimization, and the integration of supercomputing resources for neuroscience. He has contributed to foundational work on spiking neuron dynamics, firing-rate models, and the theoretical underpinnings of neural network behavior.

• Optimization methods
• Metaheuristics
• Heuristics
• +12 more

Mina Mirhosseini is a Research Fellow at the Faculty of Logistics, Molde University College, Norway. She holds a PhD in Computer Science from Shahid Beheshti University of Tehran, Iran, and has prior academic experience as a faculty member and lecturer in Iran and as a remote teaching assistant at the University of Hertfordshire, UK. Her primary research interests include Optimization Methods, Metaheuristics, Heuristics, Linear Integer Programming, Parallel Processing, Machine Learning, Artificial Intelligence, and Logistics. She has made significant contributions to solving complex computational problems such as the n-similarity problem and Mixed Integer Linear Programming (MILP) models using hybrid and parallel algorithms, particularly in the context of high-level synthesis and wireless sensor networks. The analysis of her recent publications reveals a strong focus on developing and applying advanced optimization techniques, especially quantum-inspired gravitational search algorithms and parallel genetic algorithms, to real-world engineering and computational challenges. Her work consistently emphasizes performance improvement, scalability, and load balancing in distributed and heterogeneous computing environments. Mina Mirhosseini has an extensive publication record in high-impact journals such as IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, Journal of Supercomputing, and Computers and Electrical Engineering. Her research has been published across a range of venues, reflecting interdisciplinary work at the intersection of computer science, electrical engineering, and applied optimization. She has actively contributed to the academic community through roles such as program committee member and executive committee member for conferences on fuzzy systems, swarm intelligence, and evolutionary computation. Her academic journey includes teaching and research roles in Iran, demonstrating a sustained commitment to higher education and scientific inquiry. Mina Mirhosseini is part of the research group focused on Planning, Optimization and Decision Support at Molde University College. Her current work continues to advance the state-of-the-art in parallel and metaheuristic optimization methods, with applications in logistics, synthesis, and sensor network design.

Mette KalagerView profile

Professor

• Cancer Epidemiology
• Colorectal Cancer Screening
• Health Economics
• +5 more

Mette Kalager is a Professor at the Department of Health Management and Health Economics within the Faculty of Medicine at the University of Oslo. Her research focuses on cancer epidemiology, colorectal cancer screening, and health economics, with extensive contributions to understanding overdiagnosis in cancer screening and optimizing colonoscopy practices. She maintains an active research program with numerous high-impact publications in leading medical journals. Her primary research interests center on cancer screening effectiveness, particularly in colorectal cancer prevention. Dr. Kalager's work explores the balance between benefits and harms of screening programs, with special attention to overdiagnosis and the development of risk prediction models. She has made significant contributions to understanding the relationship between adenoma detection rates and subsequent cancer risk, as well as the impact of artificial intelligence on colonoscopy quality. Her research portfolio demonstrates a consistent focus on evidence-based approaches to cancer prevention and healthcare decision making. Analysis of her recent publications reveals a strong emphasis on methodological approaches to evaluating cancer screening programs, with increasing integration of artificial intelligence applications in endoscopy. Her work spans epidemiological modeling, clinical trials, systematic reviews, and health policy analysis, consistently addressing critical questions about the effectiveness and efficiency of cancer prevention strategies. The research demonstrates a trajectory toward more personalized approaches to cancer screening based on individual risk assessment. Dr. Kalager leads or participates in multiple significant research projects including COLONIZE (focusing on Clostridioides difficile infection treatment), EPoS (European Polyp Surveillance), EndoBRAIN International, I-SCAN (Scandinavian study on IBD and cancer), NordICC (Nordic-European study on colorectal cancer prevention), and several others addressing gastrointestinal diseases, cancer risk, and healthcare interventions. Her collaborative approach is evident through extensive co-authorship with researchers across Europe and beyond.

• Programming Theory
• Algebraic Methods in Software Development
• Programming Language Design
• +12 more

Magne Haveraaen is a Professor in the Department of Informatics at the University of Bergen, where he leads the Bergen Language Design Laboratory (BLDL). He is actively engaged in research on programming theory, formal methods, language design, and high-integrity systems. His work centers on algebraic specifications, generic programming, domain engineering, and the design of the Magnolia programming language. He has been a long-standing instructor of key courses such as INF220 (Program Specification) and INF222 (Programming Languages), and contributes to national and international academic forums. University: University of Bergen School: Faculty of Mathematics and Natural Sciences Department: Department of Informatics Academic Rank: Professor Email: magne.haveraaen@uib.no His research interests include programming language design, formal specifications, generic and mouldable programming, multicore and GPU computing, and algebraic methods in software development. He emphasizes correctness, reuse, and high-integrity software through theoretical and practical innovations. He has contributed extensively to the literature on algebraic reasoning, array programming, and domain-specific abstractions. The recent publications highlight a consistent focus on algebraic semantics, language design (especially Magnolia), array programming, and formal verification. Themes across these works include correctness by design, specification-driven development, and adapting programming models for modern architectures. His involvement in workshops like ARRAY, WGP, and CALCO underscores his leadership in programming language theory. No scientific awards are listed in the provided texts. He advises students and welcomes collaboration in programming theory, language design, and formal methods. He has led funded projects such as MoSIS and SHIP and is involved in the High Integrity Systems Forum. He organizes and participates in key conferences including NWPT, GPCE, and SCAM. He leads the Bergen Language Design Laboratory (BLDL), which focuses on experimental language design, and contributes to the SAGA initiative on scientific computing with algebraic abstractions. His work integrates theory with practical tooling and language implementation.

Victor GreiffView profile

Professor

• Computational Immunology
• Adaptive Immune Receptor Repertoires
• Machine Learning in Antibody Design
• +5 more

Victor Greiff is a Professor at the University of Oslo's Institute of Clinical Medicine , leading the Computational and Systems Immunology group. His research focuses on integrating high-throughput single-cell sequencing , artificial intelligence , and high-dimensional statistics to advance therapeutic antibody design, diagnostics, and vaccine development for infectious diseases, autoimmunity, and cancer. Key Research Areas : Computational immunology, adaptive immune receptor repertoires, machine learning in antibody discovery, bioinformatics, structural biology, and immune diagnostics. Funding : European Research Council (ERC) Grant AB-AG-INTERACT (Project ID: 101125630). Recent Publications highlight interdisciplinary work in: Machine learning for antibody design and immune repertoire classification . Structural analysis of antibody-antigen interactions and evolutionary immunogenetics . Development of bioinformatics tools (e.g., ExpoSeq, TCRpower, CompAIRR) for immune sequencing data analysis. Scientific Contributions include: Establishing computational frameworks for synthetic immune repertoire generation. Exploring genetic constraints on TCR and BCR diversity. Advancing AI-driven antibody engineering for improved therapeutic developability.

Hoai Phuong HaView profile

Professor

• Green and energy-efficient computing
• Parallel and distributed computing systems
• Cyber-physical systems
• +3 more

Professor Hoai Phuong Ha is affiliated with UiT The Arctic University of Norway's Department of Computer Science. A leading expert in green computing and cyber-physical systems, they contribute to Arctic research through the Distributed Arctic Observatory (DAO) and Arctic Green Computing (AGC) group. Founded ARC (Arctic Center for Sustainable Energy) PI in EU FP7 EXCESS and H2020 TAILOR projects WP-leader in EEA POLNOR HAPADS and RCN PREAPP projects Their research focuses on energy-efficient computing, including IoT systems, edge computing, and parallel algorithms. Recent work addresses wireless charging trajectories (eU2U, 2025), smart grid networks (GridWatch, 2024), and pollution monitoring (2024). Publications span cyber-physical observatories, sensor calibration, and distributed systems optimization. Key trends in their 15 most recent articles (2017-2025) include: energy-aware data structures, Arctic-adapted IoT deployments, and sustainable computing methods. Collaborations span EU and Norwegian grants with applications in smart grids, environmental sensing, and high-performance computing. Co-founder of Arctic Center for Sustainable Energy (2017) Active in EEA POLNOR (2019-2023) and RCN eX3 infrastructure project Their lab (Realfagbygget A237) develops systems for Arctic tundra monitoring, including UAV-powered networks and energy-harvesting protocols. Students include researchers from multiple international collaborations.

• Cyber-Physical Systems
• Internet of Things (IoT)
• Edge Computing
• +7 more

Issam Rais is an Associate Professor in the Department of Informatics at UiT The Arctic University of Norway, located in Tromsø. He is an active member of the Cyber Physical Systems (CPS) research group and contributes to the Distributed Arctic Observatory (DAO) project, focusing on sustainable and efficient distributed systems in extreme environments. His research centers on cyber-physical systems, Internet of Things (IoT), edge computing, and energy-efficient data dissemination. He investigates power modeling, wake-up mechanisms, and adaptation strategies for IoT and CPS nodes operating in resource-constrained Arctic conditions. His work integrates practical system design with analytical modeling and sustainability principles. His recent publications (2019–2024) reveal a strong trend in optimizing communication, energy usage, and deployment strategies for distributed systems in harsh environments. Topics include LoRa protocol adaptation, UAV-assisted networking, compression for data-intensive applications, and simulation frameworks like ESDS for Arctic tundra scenarios. His work is consistently published in IEEE venues and high-impact journals. Scientific Awards and Recognition: No specific awards mentioned in the provided text. Advising and Grants: While no formal list of students or grants is provided, his extensive collaborative research across multiple projects and publications suggests active involvement in research leadership and project-based funding. He is deeply involved in the Distributed Arctic Observatory, which likely involves external funding and team coordination. Labs and Research Teams: Dr. Rais is a key member of the Cyber Physical Systems (CPS) research group at UiT. His work is closely tied to the Distributed Arctic Observatory (DAO) project, which aims to develop sustainable IoT-to-Extreme-Edge infrastructure for environmental monitoring in the Arctic. His lab work involves real-world deployment, power monitoring, and simulation of distributed systems.