Adrian Kirkeby is a Research Fellow at the Numerical Analysis and Scientific Computing Department of Simula Research Laboratory , where he focuses on inverse problems, wave mechanics, and applied mathematical physics. His work bridges theoretical analysis with computational methods for physical systems. Research Areas: Inverse Problems, Wave Dynamics, Numerical Analysis, Mathematical Physics Recent Publications (2024) explore seabed imaging through water wave scattering, dispersive multiplier equations for wave theory, acousto-electric tomography feasibility, and foundational inverse problems in Feynman's framework. His work emphasizes mathematical rigor and computational implementation.
Alexander Horsch is a full-time Professor at the Department of Informatics, UiT The Arctic University of Norway. His research bridges Artificial Intelligence with Medical Imaging , Biosignal Analysis , and Public Health Informatics , with a focus on Accelerometer Data Processing and Machine Learning Applications in healthcare. Recent publications demonstrate expertise in: Accelerometer-based Physical Activity Monitoring (e.g., Automatic time in bed detection , Sedentary time estimation ) Deep Learning Architecture (e.g., packetLSTM , GUNet++ , Aux-Drop ) Interpretability in AI (e.g., Propagating Transparency , T-MIS: Transparency Adaptation ) Biomedical Applications (e.g., Virtual Labeling of Mitochondria , Digital Staining ) Collaborations span multiple institutions through projects like: VirtualStain - AI solutions for label-free biomedical imaging ACTIHEALTH - Physical activity epidemiology in Arctic populations Fit Futures - Adolescent health cohort studies He actively participates in research groups including: Computational Analytics and Intelligence (CAI) Health Informatics and Technology (HIT) High North Population Studies
Morten Jakobsen is a Professor in Reservoir Geophysics at the Department of Geosciences, University of Bergen. His research focuses on advanced geophysical modeling and inversion techniques, particularly in anisotropic media, seismic full waveform inversion (FWI), and electromagnetic logging. He has contributed to computational methods for seismic and medical ultrasound imaging, emphasizing nonlinear inverse scattering and iterative solution strategies. Jakobsen’s work integrates mathematical physics with practical reservoir engineering challenges, addressing topics like fracture parameter estimation, domain decomposition preconditioning, and matrix-free algorithms for large-scale problems. His team collaborates across institutions, including NORCE Norwegian Research Centre, on projects such as 3D geological interpretation for well geosteering (3D GiG) and the DigiWells initiative. Funded by the Research Council of Norway (projects 309589, 336385, 267769), his research spans academic journals like Geophysical Prospecting and Geophysics, with a strong emphasis on computational efficiency and convergence in inverse problems. Education : Not explicitly stated in texts, but his academic role implies PhD in Geophysics or related field. Research interests include reservoir characterization, anisotropic elastic media modeling, and innovative preconditioning techniques to enhance computational workflows. He leads efforts in developing robust algorithms for seismic and electromagnetic data inversion, with applications in both subsurface exploration and medical imaging contexts. Key projects involve collaborative frameworks with industry and academia, focusing on accelerating forward modeling and inversion processes through advanced numerical methods. His publications highlight contributions to the theoretical foundations of FWI and practical implementations in heterogeneous media.
Hans Garman Torp is a Professor at the Norwegian University of Science and Technology (NTNU), serving as the Head of the Ultrasound Research Group. His work focuses on medical ultrasound technology, Doppler blood flow imaging, and applications in critical care and pediatric cardiology. He leads research initiatives at the AHL-senteret (AHL Center), contributing to advancements in cerebral blood flow monitoring, neonatal care, and resuscitation medicine. Research Interests: Medical ultrasound technology Doppler imaging for cerebral and cardiovascular monitoring Wave propagation and beam forming in ultrasound systems Development of novel Doppler devices for clinical applications Recent Work Trends: His publications highlight innovations in real-time hemodynamic monitoring during cardiac surgery, pediatric interventions, and cardiac arrest scenarios. Key areas include continuous cerebral Doppler monitoring in infants with congenital heart disease, hands-free Doppler systems for resuscitation, and quantitative assessment of valvular regurgitation using 3D ultrasound. Scientific Contributions: Over 30 peer-reviewed articles since 2020, including studies in Pediatric Research , Ultrasound in Medicine and Biology , and Journal of Clinical Medicine . His work emphasizes translational research bridging engineering and clinical medicine. Advising & Labs: Supervises PhD students like Martin Leth-Olsen and Jahn Frederik Grue. Collaborates closely with the AHL Center and NTNU’s medical engineering teams to develop next-generation ultrasound technologies.
Ida-Marie Høyvik is a Professor at the Department of Chemistry, Faculty of Natural Sciences, Norwegian University of Science and Technology (NTNU). She leads research in electronic structure theory, specializing in cost-effective computational methods for molecular systems, including coupled-cluster wave functions and multilevel descriptions. Her current focus is on wave function models for open molecular systems interacting with their environment. She is funded by the Research Council of Norway and the Center for Basic Research as a Young CAS Fellow (2022-2024). Education: PhD in Chemistry from Aarhus University (2013), Master's in Chemistry from NTNU (2010). Her work includes developing the 'eT' electronic structure program, advancing linear-scaling multilevel Hartree-Fock methods, and studying redox processes through particle-breaking wave functions. She has supervised numerous graduate students and contributed to interdisciplinary education initiatives linking mathematics and engineering. Research interests span electronic structure theory, quantum chemistry, and computational methods for large molecular systems. Her publications emphasize localization of molecular orbitals, coupled cluster theory, and real-time approaches for multiphoton processes. She actively participates in conferences and serves on academic committees.
Vetle Amundsen Vikenes is a Doctoral Research Fellow (Research Fellow) at the University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. His academic interests include cosmology, large-scale structure formation, Cosmic Microwave Background (CMB), and Cosmic Infrared Background (CIB). He has served as a teaching assistant (TA) for courses such as AST2000 (Introduction to Astrophysics), FYS2160 (Thermodynamics and Statistical Physics), and FYS2130 (Oscillations and Waves). Education: MSc in Astrophysics (2022–2024) and BSc in Physics and Astronomy (2018–2021), both from the University of Oslo. His MSc thesis focused on an emulation-based model for the projected correlation function. No scientific awards or publications are explicitly listed in the provided texts.
Runar Almaas is a Professor II at the Pediatric Research Institute, Faculty of Medicine, University of Oslo. He is actively affiliated with Oslo University Hospital (OUS), Rikshospitalet, where his research is centered on pediatric liver diseases, transplantation outcomes, and neonatal neurology. His research interests are highly specialized and impactful, focusing on pediatric hepatology , particularly cholestatic diseases and Aagenaes syndrome, liver transplantation and its long-term effects on neurocognition and allergy development, and the fundamental mechanisms of bilirubin-induced neurotoxicity in human neuronal models. His work bridges clinical observation with basic science, utilizing advanced techniques like iPSC-derived liver organoids and metabolomic profiling of dried blood spots. The trends in his recent articles (2020-2024) show a strong emphasis on developing and applying innovative diagnostic and research platforms. A key theme is the use of organoid technology to model liver function and test drug effects. Another major focus is on biomarker discovery for rare pediatric liver conditions, using non-invasive methods like dried blood spots and advanced imaging. His work consistently aims to improve the diagnosis, monitoring, and treatment of complex pediatric liver and metabolic disorders. Scientific Contributions and Recognition: His research has been published in high-impact journals such as Journal of Hepatology , Pediatric Research , and Liver Transplantation . He has made significant contributions to understanding the etiology of Aagenaes syndrome and the complications following pediatric liver transplantation. Advising and Collaborative Network: Dr. Almaas has a vast collaborative network, frequently publishing with researchers from OUS and across the Nordic region. His work on multicenter studies indicates a leadership role in collaborative clinical research. While specific student names are not listed, his senior authorship on numerous publications suggests he plays a significant role in mentoring junior researchers and clinicians. Laboratories and Research Teams: His work is conducted within the Pediatric Research Institute at OUS, Rikshospitalet. His research teams appear to be interdisciplinary, combining expertise in pediatrics, hepatology, neurology, molecular biology, and clinical chemistry. His publications on microdialysis in liver transplants and neuronal cell culture point to specialized laboratory facilities for both clinical monitoring and in vitro experimentation.
Jon Andreas Støvneng is an Associate Professor in the Department of Physics at the Norwegian University of Science and Technology (NTNU). His research spans multiple areas of physics and materials science, with a particular focus on computational approaches to understanding surface phenomena and materials properties. Støvneng's research interests primarily focus on surface science , materials science , and computational chemistry . His work frequently employs density functional theory (DFT) calculations to investigate adsorption processes, surface reactions, and material properties. He has also made significant contributions to physics education , particularly in improving laboratory instruction and student feedback mechanisms. His research has applications in catalysis, corrosion science, nanotechnology, and environmental engineering. His publication record shows consistent output spanning over two decades, with recent work focusing on cruise ship emissions, rare earth alloys, chromium oxide surfaces, and physics education methodology. His collaborative approach is evident in numerous co-authored publications across different institutions and disciplines. Notable research contributions include: Comprehensive studies on adsorption processes on chromium oxide surfaces Investigations of rare earth alloys and their catalytic properties Research on shape memory alloys and their surface interactions Development of educational resources including a physics textbook Støvneng has supervised numerous master's students, including Herman Skogseth, Cecilia H Gabrielii, and Andreas Sørbrøden Talberg, indicating his active role in mentoring the next generation of physicists and engineers. His teaching portfolio spans quantum mechanics, classical mechanics, thermal physics, and specialized courses in nanotechnology and bio-physics. His work bridges theoretical and applied physics, connecting fundamental surface science with practical applications in environmental technology, materials engineering, and educational methodology.
Cecilie Røe is a Professor II at the University of Oslo's Faculty of Medicine, Department of Physical Medicine and Rehabilitation. She is a specialist in physical medicine and rehabilitation with extensive experience in research and clinical practice focused on painful conditions in the musculoskeletal system and rehabilitation of traumatic brain injuries. Her educational background includes: MD from University of Oslo in 1987 PhD from University of Oslo in 2001 Dr. Røe's research primarily focuses on understanding mechanisms, clinical course, and effects of interventions on pain, function, activity, and participation in patients with painful musculoskeletal conditions and those recovering from traumatic brain injuries. She leads the CHARM (Research Centre for Habilitation and Rehabilitation Models & Services) project, which aims to improve rehabilitation services through evidence-based models and service organization. Her work often involves large-scale collaborative studies such as the CENTER-TBI initiative, examining various aspects of traumatic brain injury from acute care through long-term rehabilitation. Analysis of her recent publications (2024-2025) reveals a strong focus on traumatic brain injury rehabilitation, with particular attention to quality of life outcomes, unmet healthcare needs, and the relationship between personality traits and recovery. Her research spans multiple disciplines including neurotrauma, rehabilitation medicine, orthopedics, and health services research. Key subfields in her work include outcome measurement, biomarker research, comparative effectiveness studies, and the development of rehabilitation models for both musculoskeletal conditions and traumatic brain injuries. Dr. Røe actively supervises medical students in clinical examination and diagnosis of the musculoskeletal system, and mentors project assignments and researcher track students in her specialized fields. She is involved in multiple research projects including 'Physical activity and graded conditioning training after mild traumatic brain injury,' 'Transitions in treatment after traumatic brain injury,' and 'Rehabilitation needs, rehabilitation services, and costs in the first year after injuries.' She is a key member of the CHARM Research Centre for Habilitation and Rehabilitation Models & Services, as well as research groups focused on rehabilitation after trauma and painful conditions in the musculoskeletal system. Her collaborative approach is evident in her numerous multi-center studies across Europe, particularly through the CENTER-TBI consortium which represents a major international effort to improve understanding and treatment of traumatic brain injuries.
Evgueni Dinvay is a Postdoctoral Fellow in the Department of Chemistry at UiT The Arctic University of Norway, specializing in computational methods for quantum systems and fluid dynamics. His research bridges mathematical physics and chemistry through advanced numerical techniques. His primary research interests include multiwavelet-based quantum chemistry , stochastic partial differential equations , and water wave modeling . Key focus areas involve developing high-accuracy algorithms for quantum systems at the basis set limit, analyzing wave propagation in ice-covered waters, and formulating Hamiltonian structures for stochastic surface waves. Recent work emphasizes computational efficiency through the VAMPyR Python library. Publications demonstrate consistent output across theoretical and applied domains, with recent trends showing increased integration of machine learning-inspired optimization (DMRG) in quantum chemistry and rigorous mathematical analysis of stochastic wave systems. The 15 most recent works span computational chemistry (40%), mathematical physics (35%), and fluid dynamics (25%). Dinvay collaborates extensively with international researchers including Luca Frediani (Tromsø), Henrik Kalisch (Bergen), and Sigmund Selberg (Bergen), primarily through the Theoretical and Computational Chemistry research group. Current projects involve multiwavelet implementations for quantum dynamics and modeling ice-sheet responses to moving loads.
Tiago Pereira is an Associate Professor at the University of Oslo's Institute of Theoretical Astrophysics, affiliated with the Rosseland Centre for Solar Physics. His research focuses on computational astrophysics, radiative transfer, and solar atmosphere dynamics. He holds a PhD in Astronomy and Astrophysics from the Australian National University (2010) and has conducted postdoctoral research at institutions including NASA ARC and Lockheed Martin ATC (2010-2012). His work leverages high-performance computing to model solar phenomena such as spicules, chromospheric heating, and transition region dynamics. Education: PhD in Astronomy and Astrophysics, Australian National University, 2010 Postdoctoral fellowships at NASA ARC, Lockheed Martin ATC, and ANU (2010-2012) His research emphasizes radiative transfer in 3D MHD simulations, with particular attention to NLTE effects and the interplay between chromospheric layers. Key projects include the development of SunnyNet—a neural network approach to radiative transfer—and analysis of data from missions like IRIS and Hinode. Recent work explores spicule dynamics, Kelvin-Helmholtz instabilities in solar plasma, and the role of unresolved fine structures in solar atmospheres. His publications highlight contributions to spectral line formation (e.g., Hϵ, Lyman series), clustering algorithms for spectral profile analysis, and computational tools like the RH 1.5D radiative transfer code. Collaborations span international teams, including studies of solar flares, UV bursts, and coronal heating mechanisms.
Nurilla Avazov is an Associate Professor of Data Science at the Inland School of Business and Social Sciences, Inland Norway University of Applied Sciences. He holds dual PhDs: a PhD in Computer Science (2021) from the University of Auckland and a PhD in Information and Communication Technology (2015) from the University of Agder. His research focuses on machine learning, predictive analytics, time series analysis, wireless communication modeling, IoT systems, e-healthcare solutions, and human activity recognition through advanced signal processing techniques. Dr. Avazov’s academic career includes significant contributions to high-impact refereed journals and conferences. His work spans theoretical developments in algorithm design, practical implementations in sensor networks, and interdisciplinary applications in healthcare and smart environments. He has developed novel trajectory-driven channel models for mm-wave systems and pioneered backscattering-based human activity recognition methods. His expertise combines data science with telecommunications engineering, addressing challenges in non-stationary channel analysis, radar systems for indoor localization, and cybersecurity through wireless signal inference. Current research trends emphasize multimodal sensor fusion, real-time activity tracking, and privacy-aware IoT architectures. Dr. Avazov has advised no formally recorded students in the provided data. His research grants and funded projects are not explicitly detailed here, though his publications indicate sustained external collaboration and funding support. He is affiliated with the Business Analytics research group at his institution.
Koen Gerard Alois Vervaeke is a Professor at the University of Oslo in the Faculty of Medicine , leading the Vervaeke Lab . His research focuses on understanding how brain circuits transform complex sensory inputs into coherent perceptions, with emphasis on dendritic integration and cortical network dynamics. 2011-2014: Janelia Farm Research Campus – Junior Fellow (Mentors: Karel Svoboda, Jeff Magee) 2007-2011: University College London – Postdoctoral Researcher (Angus Silver Lab) 2002-2007: University of Oslo – PhD in Physiology (Johan Storm Lab) Research Themes Neocortical processing of sensory information Dendritic integration of internal and external inputs Computational modeling of neural circuits Optogenetic manipulation in perceptual tasks In vivo two-photon imaging and electrophysiology Article Trends (2007-2025): Focus on hippocampal spatial coding (VIP interneurons, place cells), neocortical sensory integration, gamma oscillations, and astrocytic Ca 2+ signaling. Methodologies include in vivo imaging, dynamic clamp, and computational modeling. Scientific Awards ERC Starting Grant (2015) FRIPRO Young Research Talents Grant (2014) The lab combines experimental (mice behavioral studies, two-photon imaging) and computational approaches to model neural circuit operations. Their work aims to establish frameworks for understanding brain dysfunction in neuropsychiatric disorders.
Ida-Marie Høyvik is a Professor in the Department of Chemistry at the Norwegian University of Science and Technology (NTNU), affiliated with the Faculty of Natural Sciences. Her research focuses on electronic structure theory, particularly developing particle-breaking wave function models for open molecular systems. She was a Young CAS Fellow (2022-2024), exploring applications of these models to redox processes. Key contributions include advancements in multilevel Hartree-Fock and coupled cluster methods, and software development (e.g., the eT program). Her teaching includes courses in quantum chemistry, nanotechnology, and data analysis in chemistry. Notable projects involve funded research from the Research Council of Norway and collaborations on educational strategies linking mathematics and engineering. Research Themes: Electronic structure models, wave function theory, redox processes, and computational methods. Awards: Young CAS Fellow (2022-2024). Supervised Theses: Includes doctoral work on real-time coupled-cluster approaches and master’s projects on multilevel Hartree-Fock and coupled cluster methodologies. Software Contributions: Co-developer of the eT electronic structure program. Recent publications highlight innovations in particle-breaking frameworks, fractional charging models, and linear-scaling implementations for large systems. Her work bridges theoretical advancements with practical applications in molecular dynamics and material science.
Simon Elias Schrader is a Research Fellow and PhD candidate in the Department of Chemistry at the University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. His research focuses on quantum mechanics, numerical methods for many-body systems, and applications in quantum computing and machine learning. He holds a Master’s degree in Computational Science: Chemistry from the University of Oslo, where he authored a thesis on Eigenvector Continuation in quantum chemistry. Education: Master’s in Computational Science: Chemistry, University of Oslo (Thesis: Eigenvector Continuation in Quantum Chemistry) Research Interests: Schrader’s work spans numerical solutions to the time-dependent Schrödinger equation, optimization techniques, and the integration of machine learning into quantum dynamics. He actively explores quantum computing frameworks and coupled cluster theory, aiming to advance computational methodologies in theoretical chemistry. Teaching: He has served as a teaching assistant for courses including KJM1101, KJM2601, KJM3310/KJM4310, KJM5600, and KJM5631 since 2022. Awards: 2022: Prize for the best Master’s thesis at the Department of Chemistry, University of Oslo Labs/Groups: Member of the Theoretical Chemistry research group at the Department of Chemistry.
