جست‌وجوی استاد و پژوهشگر

Yngve Birkelundمشاهده پروفایل

استاد

• Renewable energy
• Wind resources
• Wind power prediction
• +۸ مورد دیگر

Yngve Birkelund is a Professor of Renewable Energy in the Department of Physics and Technology at UiT The Arctic University of Norway. He serves as the Head of the UiT Arctic Center for Renewable Energy and Greenhouse Gas Management (ARC). His research focuses on renewable energy systems, particularly wind energy in Arctic conditions, and he leads significant research projects including Smart Senja and nICE. His research interests include: Renewable energy Wind resources assessment Wind power prediction Time series analysis Machine learning applications Statistical signal theory Microwave and ultrasound technologies Professor Birkelund's recent publications demonstrate a strong focus on numerical weather prediction, particularly using the Weather Research and Forecasting (WRF) model, for wind resource assessment and atmospheric icing conditions in complex terrain. His work bridges meteorology, renewable energy engineering, and data science, with significant contributions to understanding wind patterns in the challenging Arctic environment. He has supervised numerous PhD and Master's students, with recent theses focusing on wind resource assessment, atmospheric icing on wind turbines, wind power forecasting, and renewable energy integration in remote communities.

• Speech Perception
• Audiovisual Communication
• Phonetics
• +۴ مورد دیگر

Dr. Dawn M. Behne is a Professor in the Department of Psychology at NTNU (Norwegian University of Science and Technology), affiliated with the Faculty of Social and Educational Sciences. She leads the Speech, Cognition & Language Group (SCaLa) , focusing on audiovisual speech perception and cognitive processing. Her research interests span speech perception , multisensory integration , language development , and cognitive neuroscience . Key topics include audiovisual speech processing in bilingualism, musical expertise effects, and human-AI conversational dynamics. She has pioneered studies on phonetic adaptation in real-time interactions and the role of visual cues in speech comprehension. Recent work (2024) explores real-time speech adaptations between humans and AI confederates, while 2023 studies highlighted gender representation biases in visual categorization tasks. Earlier research (2019–2020) investigated musical expertise's impact on multisensory speech perception using ERP methods. Behne has published over 50 peer-reviewed articles in journals like Journal of the Acoustical Society of America , Frontiers in Psychology , and PLOS ONE . Her lab collaborates with institutions globally, including the University of Alberta (Jongman/Sereno group) and the University of Helsinki (Gabriel/Hyönä team). Grants and funding details are not explicitly listed, but her active research program indicates sustained institutional and likely external support. Current projects likely involve AI-driven speech analysis and cross-linguistic perceptual studies.

• Blockchain Technology
• Sustainable Manufacturing
• Lean Management
• +۵ مورد دیگر

Halvor Holtskog is a Professor at the Norwegian University of Science and Technology (NTNU) in the Department of Industrial Economics and Technology Management under the Faculty of Economics. His research focuses on blockchain technology, sustainable manufacturing, lean management, innovation systems, and organizational interoperability. He has authored/co-authored books such as *Blockchain as a Technology for Environmental Sustainability* (2024) and *Digitalization and Sustainable Manufacturing: Twin Transition in Norway* (2024). His work spans interdisciplinary areas including cybersecurity, healthcare informatics, and socio-technical systems. Holtskog teaches courses like IDS4006 (Organizational Change and Leadership) and SMF3091 (Sustainable Strategies). His recent publications emphasize blockchain’s role in environmental sustainability and supply chain innovation. Research Interests: Application of blockchain in environmental and healthcare contexts Lean and digital transformation in manufacturing Cybersecurity culture and organizational resilience Interoperability in smart service ecosystems Key Articles Trends: Holtskog’s recent work (2020–2024) highlights blockchain’s potential for sustainable innovation, cybersecurity resilience, and lean management adaptations. He frequently collaborates with institutions like NTNU and international researchers on topics such as supply chain transparency, healthcare decision-making frameworks, and organizational learning. Teaching & Advising: Active in master’s supervision (IDS4100) and course leadership in digital organizational structures (IØ6571). His teaching emphasizes innovation, sustainability, and organizational change. No grants or student advisee lists are explicitly detailed in the provided texts. Labs/Teams: Involved in learning factory initiatives (e.g., Roller Skis Assembly Line project) and contributes to interdisciplinary research teams focusing on socio-technical experiments and workplace innovation.

• Bayesian Statistics
• Quantitative Genetics
• Animal Behavior
• +۵ مورد دیگر

Stefanie Muff is a Professor in the Department of Mathematical Sciences at NTNU, specializing in Bayesian statistical methods, quantitative genetics, and ecological modeling. Her work bridges statistical methodology with applications in evolutionary biology and wildlife research. Expertise: Bayesian inference, missing data modeling, genomic prediction, and habitat selection analysis Key affiliations: NTNU's Department of Mathematical Sciences Research interests focus on developing statistical frameworks for ecological and evolutionary questions, including studies on animal movement patterns, inbreeding effects in wild populations, and genomic prediction in non-model organisms. She actively contributes to methodological advancements in handling missing data and measurement error in ecological datasets. Recent work emphasizes applying Bayesian models to real-world ecological problems, such as dispersal genetics in vertebrate metapopulations and spatial variation in metabolic traits. Her interdisciplinary approach integrates statistics with empirical studies on birds and other wildlife. Notable collaborations: Alpine ibex conservation, house sparrow metapopulation studies, and genomic prediction frameworks Advocates for transparent science through teaching courses like ISTT1003 - Statistics and TMA4268 - Statistical Learning. Active in academic outreach with documentaries and workshops on ecological data analysis.

Pernille Merethe Sire Seljomمشاهده پروفایل

دانشیار

• Energy Systems
• Renewable Energy
• Sustainable Development
• +۳ مورد دیگر

Pernille Merethe Sire Seljom is an Associate Professor in the Section for Energy Systems at the University of Oslo. Her research focuses on energy systems, renewable energy integration, and sustainable development, with a particular emphasis on low-carbon transition strategies and energy policy. She holds a PhD and has extensive experience in energy system modeling, demand response analysis, and climate change mitigation. Her work bridges technical and socio-economic aspects of energy systems, addressing challenges such as renewable energy variability, offshore wind potential, and energy demand reduction scenarios aligned with the Sustainable Development Goals (SDGs). She has published extensively in high-impact journals and conferences, with notable contributions to energy system flexibility, transmission infrastructure, and decentralized energy solutions in developing regions. Key research trends in her publications include the optimization of energy systems under climate constraints, the role of end-use flexibility, and the analysis of renewable energy technologies in bridging supply-demand gaps in sectors like manufacturing. Her work often employs advanced modeling techniques, including stochastic approaches and machine learning, to enhance energy system resilience and sustainability. Awards: None explicitly listed. Grants/Advising: Supervised research projects on energy transition pathways and energy policy frameworks. Active in interdisciplinary collaborations on sustainable energy solutions. She contributes to international conferences and journals, maintaining a strong focus on practical applications of energy research to real-world policy and infrastructure challenges.

Sara Pernille Jensenمشاهده پروفایل

پژوهشگر ارشد

• Philosophy of Science
• Philosophy of Physics
• Machine Learning
• +۴ مورد دیگر

Sara Pernille Jensen is a Research Fellow at the University of Oslo, affiliated with the Department of Philosophy, Classics, History of Art and Ideas within the Faculty of Humanities. Her research focuses on the epistemic role of machine learning models in science, particularly deep learning, examining how these models contribute to scientific understanding, theory development, and explanation. She investigates the boundaries between predictive capabilities and explanatory power in scientific contexts. Background: Holds an Integrated MSci in Physics and Philosophy from the University of Bristol (2021) and an MSci in Computational Physics from the University of Oslo (2023). No scientific awards or grants are explicitly listed in the provided texts. Her current research emphasizes the limitations and potentials of machine learning in advancing scientific knowledge beyond mere prediction.

Claudia Ciconeمشاهده پروفایل

استاد

• Cosmology
• Extragalactic Astrophysics
• Submillimeter Astronomy
• +۳ مورد دیگر

Claudia Cicone is a Professor in Cosmology at the Department of Astrophysics, University of Oslo's Faculty of Mathematics and Natural Sciences. Her work centers on extragalactic astrophysics and cosmology, with particular expertise in submillimeter astronomy and telescope instrumentation development. Her research interests span cosmology, extragalactic astrophysics, submillimeter astronomy, and telescope instrumentation. Professor Cicone focuses on galaxy formation and evolution, active galactic nuclei, and the development of observational techniques for next-generation telescopes. Much of her recent work centers on the Atacama Large Aperture Submillimeter Telescope (AtLAST) project, where she contributes to both scientific planning and technical design aspects. Analysis of her recent publication record reveals a strong focus on the AtLAST telescope project, with multiple 2024-2025 papers covering its scientific capabilities, optical design, and potential applications across astronomy. Her work spans time-domain astronomy, galaxy evolution studies, AGN physics, and technical aspects of submillimeter observations. The publications demonstrate significant collaboration across international teams working on cutting-edge astronomical instrumentation. Professor Cicone is actively involved with the Cosmology and extragalactic astrophysics research group at UiO and leads work on the AtLAST project, which represents a major next-generation facility for submillimeter astronomy. Her work bridges theoretical cosmology with practical observational techniques, positioning her at the forefront of developing capabilities for future astronomical discoveries.

Victor Cruces Chamorroمشاهده پروفایل

پژوهشگر ارشد

• Molecular Simulations
• Machine Learning
• Electrolyte Solutions
• +۳ مورد دیگر

Victor Cruces Chamorro is a Research Fellow at Njord, working on the ChemFrac project. His research focuses on applying machine learning regression models to study mineral rock erosion at molecular levels, leveraging his expertise in molecular simulations and non-polarizable potential development for electrolyte solutions. His background includes a bachelor's, master's, and doctoral thesis in molecular simulations, with strong programming skills supporting his computational research. Education highlights include advanced degrees focused on developing non-polarizable potentials for electrolyte systems, foundational to his current work integrating machine learning into material science challenges.

• Membrane Science
• Water and Wastewater Treatment
• Environmental Engineering
• +۸ مورد دیگر

Zakhar Maletskyi is an Associate Professor in the Department of Building and Environmental Technology at the Norwegian University of Life Sciences (NMBU). His work bridges environmental engineering, interfacial science, and data analytics, with a focus on developing advanced membrane technologies for clean water production and resource recovery. Department: Building and Environmental Technology University: Norwegian University of Life Sciences (NMBU) Research Lab: Water Process Engineering Group His research centers on membrane science, particularly nanofiltration, electromembrane processes, and sustainable membrane systems. He investigates the permeability-selectivity trade-off, leverages machine learning for process optimization, and explores natural polymer-based membranes. His work integrates interfacial and colloid science , electrochemical oxidation , and cognitive analytics to improve water treatment efficiency and resilience under climate change. The recent publications reflect a strong trend in data-driven environmental engineering , micropollutant removal , and biological-membrane hybrid systems . Themes include membrane fouling analysis, flux enhancement, biochar applications, and nitrogen removal biotechnology, indicating a holistic approach to water quality and process sustainability. Dr. Maletskyi leads and participates in several major research projects funded by The Research Council of Norway, including TIES, PATCHER, AECo, MEMPREX, and IntelMem, focusing on tunable ion separations, micropollutant removal, and intelligent membrane systems. He also contributes to education as course responsible for THT 271 and THT 310, and co-instructor for multiple advanced water technology courses. He advises graduate students in environmental and water engineering, though specific names are not listed. His lab, the Water Process Engineering group, conducts experimental and computational research on membrane processes and water treatment systems.

• 3D City Modeling
• Laser Scanning
• Photogrammetry
• +۷ مورد دیگر

Hongchao Fan is a Professor in the Department of Civil and Environmental Engineering at the Faculty of Engineering, Norwegian University of Science and Technology (NTNU). His research focuses on 3D city modeling (digital twins) , laser scanning , photogrammetry , and spatial data mining using crowdsourcing data , with applications in urban infrastructure, cultural heritage preservation, and solar energy assessment. Key research areas: 3D building reconstruction from point clouds Knowledge graph integration for urban data OpenStreetMap (OSM) data inequality analysis Solar energy potential modeling in Nordic cities Machine learning for geospatial applications Current projects: Digitalization for Culture Heritage (2025-2028, Norwegian Directorate for Higher Education and Skills) MobilityLab Stor-Trondheim (MoST) (2024-2026, digital twins for transportation models) Hellios (2022-2024, solar energy in Nordic cities) Deep learning 3D building models (NTNU Digital, 2021-2025) Technical expertise: Airborne Laser Scanning (ALS) data processing, semantic facade reconstruction, photogrammetric algorithms, and GIS-based decision support systems. He serves as Associate Editor for Big Earth Data and Geo-Spatial Information Science .

Myrthe Erica Jeanne Tillemanمشاهده پروفایل

پژوهشگر

• Immersive Multimedia
• Robotics
• Machine Learning
• +۷ مورد دیگر

Myrthe Erica Jeanne Tilleman is a Doctoral Research Fellow at the Department of Informatics, University of Oslo, within the Faculty of Mathematics and Natural Sciences. She works in the Sustainable Immersive Networks Lab (SINLAB) under the supervision of Prof. Özgü Alay and Prof. Carsten Griwodz, focusing on the integration of brain-computer interfaces for latency hiding in immersive multimodal teleoperations. Degree: MSc in Human Neuroscience, University of Turku, Finland (2021–2023) Thesis: "Towards Bidirectional Lower Limb Prostheses: Restoring Proprioception Using EMG Based Vibrotactile Feedback" at Össur, Iceland Bachelor: BSc in Technology and Liberal Arts & Sciences, University College Twente, University of Twente, The Netherlands (2016–2019) Exchange: Waseda University, Japan Her research spans interdisciplinary domains including immersive multimedia, robotics, machine learning, neuroscience, motion capture, human-robot interaction, and brain-computer interfaces . Her work aims to enhance teleoperation systems by leveraging neural feedback to mitigate latency issues in real-time immersive environments. Although no publications are currently listed in Cristin, her ongoing doctoral research suggests a strong trajectory in human-centered networked systems and neuro-interactive technologies. She is actively contributing to advanced research in sustainable immersive networks, with potential applications in prosthetics, virtual reality, and remote robotics. Scientific Affiliations: Sustainable Immersive Networks Lab (SINLAB) Networks and Distributed Systems group, Department of Informatics Faculty of Mathematics and Natural Sciences, University of Oslo She has no listed scientific awards or advisees at this stage of her career. Her research is supported by academic supervision and institutional infrastructure at UiO.

Trung Tuyen Truongمشاهده پروفایل

استاد

• Several Complex Variables
• Dynamical Systems
• Algebraic Geometry
• +۷ مورد دیگر

Trung Tuyen Truong is a Professor of Mathematics at the University of Oslo, Faculty of Mathematics and Natural Sciences, Department of Mathematics, since September 2023. Previously, he served as an Associate Professor at the same institution from September 2017 to September 2023. His academic journey includes a PhD from Indiana University (2006-2012) under Professor Eric Bedford, followed by postdoctoral positions at Syracuse University (2012-2014), Korea Institute for Advanced Study (2014-2015), and The University of Adelaide (2015-2017). His research spans several complex variables, dynamical systems, and related topics in algebraic geometry. He is particularly interested in applying dynamical systems theory and computational techniques to solve both theoretical and practical problems. His work includes developing optimization algorithms, exploring connections between Weil's Riemann hypothesis and dynamical systems, and investigating the Jacobian conjecture. Truong has made significant contributions to optimization methods, particularly developing Backtracking Gradient Descent and Backtracking New Q-Newton's method, which have applications in deep learning and solving complex mathematical problems. His research has been supported by various grants including a Seed grant from UiO Growth House for developing AI tools for mathematical education, membership in MSCA-Cofund-DP for ERC funding PhD positions, and as Principal Investigator for the Young Research Talents grant from the Research Council of Norway. Truong is actively involved in developing software tools to help students learn mathematics and optimization, as well as creating efficient computational tools. His NUV Optimization solvers have demonstrated superior performance compared to established tools like Matlab and IPOPT in certain optimization problems. He is collaborating with AI companies and medical doctors to use AI for diagnosing from lung data measurements. His publications demonstrate expertise across pure mathematics, computational methods, and interdisciplinary applications, with recent work focusing on dynamical systems approaches to number theory problems and practical optimization algorithms.

• Atmospheric Science
• Meteorology
• Infrasound
• +۴ مورد دیگر

Dr. Ekaterina Vorobeva is a researcher at the Department of Physics, Norwegian University of Science and Technology (NTNU) in Trondheim, Norway, with additional affiliation to NORSAR in Kjeller, Norway. Her research focuses on applying machine learning techniques to analyze infrasound data for studying stratospheric dynamics, particularly the polar vortex. Dr. Vorobeva's research interests center on atmospheric science, with special emphasis on stratospheric dynamics, polar vortex analysis, and the application of machine learning to meteorological data. She bridges physics, meteorology, and computational science to develop novel methods for monitoring upper atmospheric phenomena using naturally occurring infrasound signals. Her recent work demonstrates how microbarom infrasound data from the International Monitoring System can be used with stochastic machine learning models to estimate stratospheric polar vortex strength. This approach provides potential for near-real-time monitoring of upper stratospheric winds, which could improve subseasonal-to-seasonal weather forecasting models. Dr. Vorobeva has made significant contributions to the field of atmospheric monitoring through her innovative application of machine learning to infrasound data. Her work offers a promising alternative approach to measuring upper atmospheric conditions where traditional observational methods are limited. Her research methodology combines expertise in physics, atmospheric science, and advanced computational techniques, positioning her at the intersection of multiple scientific disciplines working toward improved understanding and prediction of atmospheric phenomena.

Torsten Hundebøl Hansenمشاهده پروفایل

پژوهشگر

• Sedimentary Basin Analysis
• Structural Geology
• CO2 Geological Storage
• +۵ مورد دیگر

Torsten Hundebøl Hansen is a Researcher in the Department of Geosciences at the University of Oslo, specializing in the Section for Study of Sedimentary Basins since his appointment as Postdoctoral Fellow in 2023. His work focuses on structural deformation processes in sedimentary basins, with particular emphasis on the North Sea region including the Danish Central Graben and Norwegian-Danish Basin. Dr. Hansen's research integrates advanced seismic interpretation with structural modeling to investigate basin evolution, salt tectonics, and fault behavior. His expertise spans porosity analysis in chalk reservoirs, basin inversion mechanisms, and the critical relationship between structural deformation and reservoir/seal properties. This work provides fundamental insights for petroleum exploration and geological CO2 storage applications. Analysis of his 15 publications (2019-2025) reveals a clear progression toward CO2 storage research, with nearly half of recent works (2023-2025) directly addressing storage site selection, seal integrity, and containment risk. The North Sea Basin remains the primary study area, with increasing utilization of machine learning techniques for fault mapping and quantitative analysis of salt-tectonic influences on storage complexes. Dr. Hansen actively contributes to two major research initiatives: the CO2SafeQuest project investigating fault properties for CO2 storage, and the CCNS (CO2 storage research and cross-border synergies) consortium focused on southern Norwegian North Sea storage potential. His work bridges fundamental structural geology with practical applications for carbon capture and storage. He operates within the Study of Sedimentary Basins research group at the University of Oslo, collaborating on projects that integrate structural analysis, seismic interpretation, and geomechanical modeling to address challenges in both hydrocarbon exploration and geological carbon storage.

Vinicius Rezende Carvalhoمشاهده پروفایل

پژوهشگر ارشد

• Predictive Coding
• Cognitive Maps
• Brain Rhythms
• +۷ مورد دیگر

Vinicius Rezende Carvalho is a Research Fellow at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion (University of Oslo). His work focuses on auditory predictive coding, cognitive maps, and seizure dynamics using intracranial EEG and computational models. Education : Bachelor's, MSc, and PhD in Electrical Engineering from Universidade Federal de Minas Gerais (UFMG), Brazil. Academic Interests : Predictive coding, brain rhythms, computational neuroscience, epilepsy, nonlinear dynamics, and speech decoding. His current research investigates the neurophysiological basis of auditory predictive coding through the AudioPred Project, analyzing single-unit recordings and local field potentials in response to auditory stimuli. Earlier work explored seizure prediction using stimulation probes and computational models. Publications highlight applications of delay differential analysis in speech decoding and seizure dynamics, alongside studies on neural entrainment and cognitive impairments in animal models. Scientific Award : 2020 Society for Neuroscience Latin American Training Program Fellow. Collaborations span institutions including the Salk Institute, University of California (Los Angeles), and Massachusetts General Hospital. He contributes to open-access datasets and participates in teaching programs like Neuromatch Academy.