جست‌وجوی استاد و پژوهشگر

Anne H Schistad Solbergمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Image Analysis
• +۶ مورد دیگر

Anne H Schistad Solberg is a Professor in the Department of Informatics at the University of Oslo's Faculty of Mathematics and Natural Sciences. She leads research in digital signal processing and image analysis, with a focus on machine learning applications across multiple domains. As co-director of SFI Visual Intelligence, she oversees research on interpretable deep learning models, uncertainty quantification, contextual learning, and self-supervised learning approaches. Her research spans medical imaging (particularly cardiovascular ultrasound), environmental monitoring using satellite imagery, and seabed mapping with sonar technology. Professor Solberg's work demonstrates a consistent trajectory from foundational signal processing techniques to cutting-edge deep learning applications. Her recent publications show increasing specialization in medical image analysis, particularly in echocardiography enhancement and cardiac structure segmentation, while maintaining strong contributions to remote sensing and geophysical applications. She teaches several popular courses including IN2070, IN3310, and IN5400 (Machine Learning for Image Analysis), which is noted as the most popular master's/PhD course on deep learning at the University of Oslo. Professor Solberg serves as principal investigator for the Intelligent Cardiovascular Ultrasound Scanner (INCUS) project, collaborating with GE Vingmed Ultrasound to develop AI-enhanced cardiac imaging systems that improve diagnostic accuracy and productivity in echocardiography. Co-director of SFI Visual Intelligence research center Principal Investigator for the INCUS project (Intelligent Cardiovascular Ultrasound Scanner) Member of the Digital Signal Processing and Image Analysis (DSB) research group Member of the Strategic Research Initiative: Multimodal Medical Imaging and Image Analysis (MEDIMA) Her research group develops algorithms that address real-world challenges in medical diagnostics and environmental monitoring, with a particular emphasis on making deep learning models more interpretable and reliable for critical applications. The INCUS project, funded through User-driven Research-based Innovation (BIA), aims to reduce the time wasted during cardiac ultrasound examinations by implementing intelligent algorithms that learn from expert users and historical data.

Behzad Alaeiمشاهده پروفایل

دانشیار

• Structural Geology
• Sedimentary Basin Analysis
• Fault Zone Architecture
• +۶ مورد دیگر

Behzad Alaei serves as an Associate Professor in the Section for Study of Sedimentary Basins within the Department of Geosciences at the University of Oslo's Faculty of Mathematics and Natural Sciences. His office is located in room K38 of the Geology Building at Sem Sælands vei 1, 0371 Oslo, with a professional email contact at behzad.alaei@geo.uio.no. Dr. Alaei maintains an active research profile with publications spanning from 2005 to the present, demonstrating his ongoing contributions to geological sciences. Dr. Alaei's research spans multiple critical areas within structural geology and sedimentary basin analysis, with particular expertise in fault zone architecture, seismic interpretation techniques, and CO2 storage site assessment. His work bridges theoretical geological concepts with practical applications in petroleum geology and carbon sequestration. A significant portion of his research focuses on the Norwegian Barents Sea region, where he has conducted extensive studies on normal fault systems and their geometric characteristics. His recent work increasingly integrates machine learning and deep learning approaches with traditional geological analysis, reflecting the evolving nature of geoscience research methodology. The analysis of Dr. Alaei's publication record from 2018-2024 reveals a strong thematic continuity in fault characterization research, with progressive incorporation of advanced computational methods. Early publications focused primarily on traditional structural analysis of fault systems in sedimentary basins, while more recent work demonstrates increasing integration of machine learning techniques for fault detection and characterization. A notable trend is the application of these geological insights to practical challenges in carbon capture and storage, particularly regarding fault risk assessment for CO2 storage sites in the North Sea region. His collaborative work with Anita Torabi appears consistently throughout this period, suggesting a strong research partnership. Dr. Alaei maintains an active research program with multiple ongoing projects related to sedimentary basin analysis and fault characterization. His work appears to involve significant collaboration with both academic and industry partners, particularly in the context of CO2 storage research. While specific grant details aren't provided in the available information, his consistent publication record across multiple high-impact journals suggests successful funding of his research activities over the past two decades.

Benjamin Ricaudمشاهده پروفایل

دانشیار

• Machine Learning
• Graph Signal Processing
• Explainable AI
• +۳ مورد دیگر

Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.

• Industrial Ecology
• Material Flow Analysis
• Urban Metabolism
• +۵ مورد دیگر

Daniel Beat Müller serves as Professor at the Industrial Ecology Programme within the Department of Energy and Process Engineering at the Norwegian University of Science and Technology (NTNU), Trondheim. His office is located at Realfagbygget Gløshaugen (E4-120) with contact details daniel.mueller@ntnu.no and +4791897755. His research centers on analyzing human needs in relation to material/energy flows and environmental impacts, with two primary focus areas: (i) urban evolution and associated material flows for managing building/infrastructure stocks, and (ii) national/global metal cycles to identify supply constraint reduction strategies. His methodology integrates design, modeling, and decision-making through transdisciplinary stakeholder engagement. Müller teaches Material Flow Analysis and Systems Analysis of the Built Environment for Industrial Ecology and Civil Engineering Master's students. His research outputs demonstrate strong trends in circular cities, critical mineral management, and urban metabolism, with recent publications emphasizing building information modeling, electric vehicle battery systems, and phosphorus cycling. His work consistently addresses resource criticality within energy transition contexts. As (ad interim) chair of the International Society of Industrial Ecology’s MFA-ConAccount section, he contributes to methodological standardization. He previously served on the U.S. National Research Council’s Committee on Defense Stockpiles and remains active in Switzerland's National Research Programme 65 "New Urban Quality". Müller supervises numerous Master's and doctoral students, with thesis topics spanning lithium-ion battery recycling, building stock dynamics, and urban resource flows. His projects frequently involve industry collaboration for practical implementation of material stewardship strategies.

Eduard Kamburjanمشاهده پروفایل

پژوهشگر

• Digital Twins
• Formal Methods
• Hybrid Systems Verification
• +۵ مورد دیگر

Eduard Kamburjan is a Researcher at the University of Oslo , affiliated with the Reliable Systems (PSY) and Data and Knowledge Systems (DKM) research groups. His work bridges formal methods , digital twin engineering , and knowledge graph applications . Research interests include: Formal verification of hybrid systems using deductive methods Digital twin architecture with compositional correctness guarantees Semantic lifting and ontology-driven modeling for complex systems Concurrency analysis and non-determinism in program verification Interactive visualization as serious games for formal methods His 2024-2023 publications demonstrate expertise in digital twin reconfiguration , semantic interoperability , and knowledge-based runtime enforcement . Key contributions include Crowbar for active object verification and ABS simulator toolchain for model-driven engineering. Collaborations span institutions like Springer , ACM , and IEEE , with work featured in Lecture Notes in Computer Science (LNCS) , Software and Systems Modeling (SoSyM) , and Science of Computer Programming . His research integrates RDF data management , behavioral contracts , and modular analysis for distributed systems.

Jim Tørresenمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Biology-Inspired Computing
• +۶ مورد دیگر

Jim Tørresen is a Professor of Computer Science at the Department of Informatics, University of Oslo, where he has been employed since 1999 (Associate Professor 1999-2005, Professor since 2006). He serves as group leader for the Robotics and Intelligent Systems (ROBIN) research group and is also a Principal Investigator at the Centre for Interdisciplinary Studies in Rhythm, Time and Motion (RITMO). His academic career includes visiting positions at Cornell University's Creative Machines Lab (2010-2011) and Kyoto University in Japan (1993-1994). His educational background includes a Dr.ing. (Ph.D.) in Computer Architecture from the Norwegian University of Science and Technology (1996) and an M.Sc. in Computer Architecture from the same institution (1991). Before his academic career, he worked in industry at Navia Aviation (1998-1999) and NERA Telecommunications (1996-1998). Tørresen's research spans artificial intelligence, robotics, and bio-inspired computing. His work focuses on biology-inspired algorithms, programmable logic (FPGA), robotics (simulation, prototyping, control), and human-robot interaction. He has made significant contributions to areas including evolutionary computing, reconfigurable hardware, and adaptive systems. His research often bridges theoretical computer science with practical applications in healthcare, music, and industrial settings. His recent publications demonstrate a strong focus on human-robot interaction, particularly in healthcare contexts for elderly care, as well as applications in sports science, musical robotics, and geological engineering. His work shows a consistent pattern of interdisciplinary research that combines machine learning techniques with domain-specific challenges. Tørresen has also authored a popular science book on artificial intelligence in the "what is" series by Universitetsforlaget, which discusses fundamental concepts, methods, future perspectives, and ethical aspects of AI. He has been active in academic leadership, serving as General Chair for the 22nd International Conference on Field Programmable Logic and Applications (FPL) in 2012 and the 9th Joint IEEE International Conference of Developmental Learning and Epigenetic Robotics in 2019. As group leader of ROBIN, he oversees research on intelligent systems that operate in dynamic environments requiring runtime adaptation. The group works at both fundamental and applied levels, using evolutionary algorithms for robot learning and machine learning techniques for classification and recognition tasks in various application domains.

Tom Christian Holm Adamsenمشاهده پروفایل

دانشیار

• Radiochemistry
• Positron Emission Tomography (PET)
• Medical Imaging Applications
• +۴ مورد دیگر

Tom Christian Holm Adamsen is an Associate Professor at the Department of Chemistry, University of Bergen. He is affiliated with the Center for Nuclear Medicine/PET at Haukeland University Hospital in Bergen. His research focuses on radiochemistry, PET imaging techniques for medical and industrial applications, pharmacology, and oncology tracers. Adamsen’s work spans pre-clinical imaging, novel radiotracer development, and the application of PET in drug distribution studies. He has contributed to projects like the Bergen Tracer Development Centre and instrumental methods in radiochemical synthesis. His research also explores interdisciplinary applications, such as using PET imaging in geological core analysis and hydrocyclone studies. Key areas of interest include titanium-45-based radiopharmaceuticals, single-cell tracking in high-noise conditions, and the repurposing of drugs like sulfasalazine for cancer treatment. His work bridges nuclear chemistry, biomedical engineering, and clinical diagnostics. Adamsen teaches courses such as KJEM260 (Radiochemistry and Radioactivity) and contributes to academic conferences and consortia like the 180°N Norwegian Nuclear Medicine Consortium. He has held roles as project manager and PI for multiple research initiatives, emphasizing translational research between academia and clinical practice.

Merethe Frøylandمشاهده پروفایل

استاد

• Science Didactics
• Geodidactics
• Fieldwork Pedagogy
• +۶ مورد دیگر

Merethe Frøyland is a Professor at the Science Center (Naturfagsenteret) within the Faculty of Mathematics and Natural Sciences (MN-fakultetet) at the University of Oslo (UiO). With a background in geology and a PhD in science education, she specializes in science didactics , geodidactics , and informal learning environments . Her work focuses on integrating other learning arenas (e.g., fieldwork, museums) into science teaching and improving teacher education through continuing professional development programs.

• Industrial Ecology
• Material Flow Analysis
• Resource Efficiency
• +۶ مورد دیگر

Daniel Beat Müller is a Professor at the Industrial Ecology Programme within the Department of Energy and Process Technology at NTNU. His research focuses on understanding material and energy flows in urban systems and global metal cycles to support sustainable development. Müller chairs the International Society of Industrial Ecology’s ConAccount section and contributes to initiatives like the Swiss National Research Programme 65 on urban quality. Education: Not explicitly stated in text. Affiliations: Industrial Ecology Programme, NTNU U.S. National Research Council’s Committee on Defense Stockpiles Swiss National Science Foundation’s NRP 65 International Society of Industrial Ecology His research integrates design, modeling, and decision-making to address urban infrastructure stocks, material criticality, and climate mitigation. Collaborations with governments and industries aim to translate academic insights into practical solutions. Recent Article Trends: Focus on critical minerals (nickel, graphite, cobalt), circular city strategies, and stock-flow analysis for decarbonization. Publications emphasize transdisciplinary approaches to energy transition challenges and urban metabolism. Awards: None explicitly mentioned. Advising & Grants: Supervises over 15 graduate students in MFA modeling, circular economy, and resource systems. Engaged in funded projects on battery recycling, urban building stocks, and phosphorus cycles. Labs/Teams: Leads the Industrial Ecology Programme’s research groups on material cycles and urban systems. Collaborates with institutions like the UNEP Resource Panel and ETH Zurich.

• Spectral Imaging
• Mathematical Morphology
• Soft Metrology
• +۳ مورد دیگر

Hilda Deborah is a Senior Researcher at the Department of Computer Science (IDI Gjøvik), Norwegian University of Science and Technology (NTNU). Her research focuses on spectral imaging, mathematical morphology, and soft metrology for image processing, with current interests in texture perception, digital humanities, and public dissemination of imaging research through interaction design. Education: She holds a double PhD in Computer Science from NTNU (2016) and a PhD in Signal and Image Processing from Université de Poitiers (2016). Professional History: Marie Curie Postdoctoral Fellow (2018-2020) under the FRIPRO Mobility program, funded by the Research Council of Norway. She is currently part of NTNU's Outstanding Academic Fellow Programme 4.0 (2022-2026). Research Interests: Her work spans spectral imaging applications in cultural heritage, including pigment analysis, hyperspectral dataset development, and museum visitor experience studies using imaging data. She also explores interdisciplinary methods for digital humanities and public engagement. Awards: Marie Curie Postdoctoral Fellowship, FRIPRO Mobility Grant (2018-2022), and participation in the FRIPRO Toppforsk project (2019-2024). Grants: FRIPRO Mobility (2018-2022) and Toppforsk (2019-2024) grants from Norway's Research Council. Projects include metrological texture analysis and collaborations on Dead Sea Scrolls research. Labs/Teams: Member of the Colourlab at NTNU, focusing on imaging science and cultural heritage applications. Leads initiatives like the Hyperspectral Pigment Dataset and interactive visualization tools for cultural heritage data.

• Reservoir Engineering
• Geoscience
• CO2 Enhanced Oil Recovery
• +۳ مورد دیگر

Dr. Zachary Paul Alcorn is an interdisciplinary reservoir engineer and geoscientist at the Department of Physics and Technology at the University of Bergen, where he serves as Research Director for the Norwegian Petroleum Research Center, NCS2030 (National Center for Sustainable Subsurface Utilization of the Norwegian Continental Shelf). His research bridges laboratory observations with field performance, focusing on multiphase fluid flow during CO2 injection processes in subsurface reservoirs. He teaches courses PTEK 211 and ENERGI 365 while leading multiple research projects including 'Optimizing CO2 Foam Mobility Control for Field Pilots' and 'In-situ Quantification of CO2 Flow and Mobility Control for Improved Carbon Utilization and Storage'. Dr. Alcorn's research interests center on CO2 foam applications for enhanced oil recovery (EOR) and carbon storage. He has developed expertise in special core analysis, reservoir characterization, geologic and reservoir modeling, numerical simulation, and field pilot design. His work specifically focuses on CO2 foam mobility control, investigating how foam can reduce gas mobility, improve CO2 utilization, and decrease gas-oil ratios in heterogeneous reservoirs. His research spans from pore-scale phenomena to field-scale implementation, with particular attention to how geological heterogeneity affects fluid flow behavior. His publication record shows a strong focus on CO2 foam technology, with research progressing from fundamental pore-scale investigations to field-scale implementation. The articles demonstrate an evolving research trajectory from basic foam characterization to practical field applications, with increasing attention to monitoring techniques and integration with carbon capture, utilization, and storage (CCUS) frameworks. Recent work emphasizes the relationship between rock properties and foam performance, surfactant formulations for challenging reservoir conditions, and methods for monitoring and interpreting field pilot results. Dr. Alcorn leads the NCS2030 research center and has been involved in multiple significant projects related to CO2 utilization and storage. His work has resulted in numerous field pilot implementations, particularly in heterogeneous carbonate reservoirs, with a focus on integrating laboratory findings with practical field applications. He actively collaborates with researchers across institutions and regularly presents findings at major industry conferences including SPE events and specialized CCUS conferences. His laboratory work focuses on pore-scale and core-scale investigations of CO2 foam behavior under reservoir conditions. He has developed methodologies for monitoring foam performance through pressure measurements and other techniques. His team works extensively with various rock types, particularly heterogeneous carbonates, to understand how geological properties affect foam generation, stability, and mobility control. Current research directions include nanoparticle-stabilized foams, surfactant formulations for high-salinity environments, and methods to quantify foam effects on CO2 storage capacity.

• Geophysics
• Porous Media Dynamics
• Fracture Network Analysis
• +۴ مورد دیگر

Joachim Mathiesen is a Professor at the NJORD Center for the Study of Earth Physics within the Department of Physics, University of Oslo. His research spans geophysics, porous media dynamics, and nonlinear fluid systems. Key collaborations include Gaute Linga, Francois Renard, and Tanguy Le Borgne. His work focuses on fracture network analysis , turbulent flow in rough channels , and electrohydrodynamic phenomena . He investigates fluid-rock interactions, solute dispersion in multiphase systems, and stress-driven phase transformations. 2024-2023 : Deep-learning analysis of rock fractures, 4D neutron imaging of sandstone transport, chaotic signatures in porous media flows 2018-2020 : Wetting control with electrolytes, electrohydrodynamic channeling effects, dynamic fluid connectivity in porous media Mathiesen applies computational modeling to geological systems, including Martian araneiform formation, rock compaction, and communication networks. His numerical schemes address energy-stable simulations of complex fluid properties.

Julius Parulekمشاهده پروفایل

دانشیار مدعو

• Molecular Visualization
• Bioinformatics
• Computer Graphics
• +۲ مورد دیگر

Julius Parulek is an Adjunct Associate Professor at the University of Bergen's Department of Informatics, specializing in molecular visualization and computational methods for biomolecular structures. His work focuses on developing tools and techniques for interactive exploration of protein dynamics, ligand transportation, and large-scale biological datasets. He collaborates with domain experts in biochemistry and structural biology to create visualization solutions for complex molecular processes. Research interests include: Interactive visualization of protein tunnels and cavities Multiscale rendering of biomolecular complexes Geological modeling and sketch-based visualization Agent-based modeling for biochemical processes Key contributions include: Development of the cellVIEW tool for large-scale biomolecular visualization Pioneering work on continuous levels-of-detail for molecular surfaces Techniques for unfolding protein tunnels to analyze their dynamics His research bridges computer graphics with structural biology, enabling scientists to better understand molecular mechanisms through advanced visualization approaches.

Kari Beate Remmenمشاهده پروفایل

دانشیار

• Science Education
• Geography Education
• Outdoor Education
• +۶ مورد دیگر

Kari Beate Remmen is an Associate Professor in the Department of Teacher Education and School Research at the University of Oslo, where she has been employed since 2016. Her academic work focuses on science and geography education with particular emphasis on outdoor learning environments, field-based teaching approaches, and the integration of sustainability concepts into educational practices. Dr. Remmen's research interests include exploratory field teaching in geosciences, outdoor education in science education and teacher training, collaboration between school teachers and external science professionals, video observation of teaching and learning in earth sciences, and using representations in science teaching. She has developed frameworks for effective fieldwork design that support student learning processes throughout preparation, fieldwork, and follow-up activities. Her extensive publication record from 2013-2024 reveals consistent focus on practical applications of educational theory. Remmen's work frequently addresses how fieldwork and outdoor learning can enhance deep learning experiences, with particular attention to the transition between classroom instruction and outdoor activities. Her research shows strong commitment to understanding how students and teachers engage with scientific concepts in real-world settings. As an educator, Remmen teaches courses in geography didactics for practical pedagogical education programs, geoscience didactics for the Geosciences program, and research methods courses. She has served as supervisor for numerous master's students and is currently guiding several PhD candidates from multiple institutions. Dr. Remmen actively collaborates with institutions including the Climate House (NHM, UiO), OsloMet, University of Tromsø, School Laboratories in Geosciences at UiO and UiB, Science Centre (UiO), and University of Sherbrooke (Canada). She currently leads the National Network for Sustainability and Education (NABU) and serves on the board of the Norwegian Geographical Society. Her research has been conducted through projects such as 'Representation and Participation in Science (REDE)' and as part of the 'Challenges of Sustainability in Educational Research (COSER)' research group, reflecting her commitment to sustainable educational practices and meaningful science learning experiences.

Helwig Hauserمشاهده پروفایل

استاد

• Visualization
• Data Science
• Interactive Data Analysis
• +۵ مورد دیگر

Helwig Hauser is a Professor at the Department of Informatics, University of Bergen, specializing in visualization and data science. His research focuses on interactive visual data exploration, flow visualization, and applications in engineering, biomedical, and geoscience domains. He leads the Visualization research group and teaches courses in computer graphics and data science. Key research interests include scientific visualization, molecular dynamics analysis, high-dimensional data exploration, and visual analytics. His work spans computational fluid dynamics, medical imaging, and engineering simulation, with a focus on developing intuitive tools for complex data interpretation. Hauser has authored/co-authored over 100 peer-reviewed publications, including influential works on flow visualization, ensemble analysis, and interactive brushing techniques. He has edited conference proceedings such as PacificVis and serves on program committees for top visualization conferences. His contributions include open-source tools like the SemSeg project and collaborations with industry on automotive and biomedical applications. He advises graduate students and collaborates internationally, emphasizing interdisciplinary applications of visualization in medicine, geology, and engineering. Current projects explore AI-enhanced visualization, real-time data exploration, and multiscale molecular analysis.