جست‌وجوی استاد و پژوهشگر

Gizem Ates Venåsمشاهده پروفایل

دانشیار

• Human-Robot Interaction (HRI)
• Inertial Measurement Units (IMUs)
• Gamified User Training
• +۴ مورد دیگر

Gizem Ates Venås is an Associate Professor in the Department of Computer Science, Electrical Engineering and Mathematical Sciences at Western Norway University of Applied Sciences. She leads research in Human-Robot Interaction (HRI), focusing on intuitive human-robot cooperation through inertial measurement units (IMUs), gamified training systems, and teleoperation. Her work emphasizes safety and natural interaction in collaborative robotics applications. Education: Bachelor's in Electronics and Communication Engineering (2010-2015), Izmir Institute of Technology Master's in Mechanical Engineering (2015-2018), Izmir Institute of Technology PhD in Computer Science (Software Engineering, Sensor Networks) (2018-2023), Western Norway University of Applied Sciences Research interests include: IMU-based motion estimation for cooperative manipulation Design of gamified training systems to improve HRI proficiency Medical robotics applications in teleoperated surgical systems Sensor fusion and real-time data processing for collaborative robots Teaching focuses on robotics fundamentals: ROS programming, embedded systems (Arduino), and robot control Courses: ELE306 Robotics, DAT160 Intelligent Robots, ADA526 Applied Robot Prototyping Labs/Teams: Core member of HVL Robotics Lab, developing open-source robotics education materials and simulation tools.

• Medical Technology
• Biomedical Engineering
• Optics
• +۵ مورد دیگر

Peyman Mirtaheri is a Professor at the Department of Mechanical, Electronics and Chemistry (MEK) under the Faculty of Technology, Art and Design at Oslo Metropolitan University (OsloMet). He also holds an Adjunct Professor position at the Faculty of Biomedical Engineering at Michigan Technological University, USA. His primary role involves teaching courses such as Medical Sensors and Actuators (ACIT4720) and the upcoming BioInspired Systems (fall 2025). Research Interests: Professor Mirtaheri leads the ADEPT lab (ADvanced hEalth intelligence and brain-insPired Technologies) and ADEPT_technology, focusing on: Developing measurement techniques for brain activity detection and analysis Applying fNIRS (functional near-infrared spectroscopy) and EEG (electroencephalography) to study movement, balance, and cerebral cortex activity Advancing bio-inspired systems and health technology Key Projects: His active research includes Use of 3D printers in future rehabilitation (knowledge-based and patient-centered solutions) and completed work on Brain activity during walking and balance . Labs & Collaborations: He integrates his optical/NIRS laboratory into OsloMet's ADEPT lab, collaborating with institutions like Michigan Technological University and contributing to global conferences such as the IEEE International Symposium on Circuits and Systems and the ISPO World Congress .

• Optical Nanoscopy
• Quantitative Phase Imaging
• Deep Learning in Microscopy
• +۵ مورد دیگر

Krishna Agarwal is a Professor in Ultrasound, Microwaves and Optics at the Department of Physics and Technology, UiT The Arctic University of Norway. His research spans multiple interdisciplinary fields including optical nanoscopy, quantitative phase imaging, and computational imaging techniques. He is an active member of the Ultrasound, Microwaves and Optics research group, with specialized focus on Optical Nanoscopy, and participates in research projects including VirtualStain and NanoAI. Professor Agarwal's research interests center on advanced imaging techniques, particularly in optical nanoscopy and quantitative phase imaging. His work bridges physics, computer science, and biology, developing novel computational methods for microscopy enhancement. His research focuses on applying deep learning to improve imaging resolution, developing frameworks for quantitative phase reconstruction, and creating new methodologies for 3D imaging of biological specimens. His work has significant applications in biomedical imaging, cellular analysis, and diagnostic technologies. Recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional optical techniques, with increasing emphasis on computational approaches to solve longstanding challenges in microscopy. His research shows consistent progression from theoretical optical methods toward practical applications in biological imaging and medical diagnostics, with numerous publications in high-impact optics and imaging journals. Professor Agarwal teaches Optisk nanoskopi (Course FYS-3029) at UiT, contributing to advanced optics education. His research group appears to collaborate extensively with international researchers across multiple institutions, suggesting active grant funding and collaborative research efforts. Based at Teknologibygget Tromsø 3.058, Professor Agarwal leads research in the Optical Nanoscopy group, focusing on developing next-generation imaging technologies that combine optical physics with computational methods. His team appears to work at the intersection of physics, computer science, and biology, developing tools that push the boundaries of what's possible in cellular and sub-cellular imaging.

Mohammad Poursinaمشاهده پروفایل

دانشیار

• Dynamic Modeling and Simulation of Multibody Systems
• Robotics and Robotic Rehabilitation
• Artificial Intelligence and Control Systems
• +۴ مورد دیگر

Mohammad Poursina is an Associate Professor at the Department of Engineering Sciences , University of Agder, Norway. His research spans multibody dynamics , robotics , and celestial mechanics , with a focus on dynamic modeling, control systems, and biomechatronics. He has held prior academic roles at the University of Arizona and Rensselaer Polytechnic Institute. Ph.D. in Mechanical Engineering, Rensselaer Polytechnic Institute (2011) M.Sc. and B.Sc. in Mechanical Engineering, University of Tehran (2006, 2003) His work addresses complex systems through interdisciplinary approaches, including robotic rehabilitation , flexible manipulators , and molecular dynamics . Recent publications highlight advancements in trajectory optimization , VR/AR surgical training , and asteroid system modeling . He serves on the editorial boards of Multibody System Dynamics and Journal of Computational and Nonlinear Dynamics , and has organized ASME conference sessions. Key research groups: Artificial Intelligence, Biomechatronics, and Collaborative Robotics Projects: Reality-Connected Machine Simulation of Heavy Machinery (RealSim)

Arild Bergesen Husebøمشاهده پروفایل

پژوهشگر ارشد

• Mechatronics
• Renewable Energy
• Machine Learning
• +۳ مورد دیگر

Arild Bergesen Husebø is a Research Fellow affiliated with the Department of Engineering Sciences at the University of Agder (UiA). His work focuses on applying machine learning techniques to the condition monitoring of electromechanical machinery, particularly in fault diagnosis and predictive maintenance. Education: Bachelor's and Master's degrees in Renewable Energy from UiA Research Interests: His research spans mechatronics, renewable energy systems, and industrial automation, with a strong emphasis on leveraging machine learning for early fault detection in machinery. Key areas include bearing diagnostics, resonance frequency analysis, and electrical fault identification in induction motors. Recent Publications: His studies highlight advancements in time-domain signal processing, convolutional neural networks, and autoencoder-based feature extraction for predictive maintenance. These works align with applications in electrical machines, wind turbines, and industrial systems.

Olav Gaute Hellesøمشاهده پروفایل

استاد

• Photonics
• Micro- and Nanotechnology
• Integrated Optics
• +۴ مورد دیگر

Olav Gaute Hellesø is a Professor and Head of the Department of Physics and Technology at UiT The Arctic University of Norway, where he has been employed since 1998 and led the department since 2021. He is a member of the Ultrasound, Microwaves and Optics research group, focusing on integrated optics and optical trapping of micro- and nanoparticles. Education : Cand. Scient. from University of Bergen (1989), DEA and Dr. from INP de Grenoble, France (1990, 1994) His research spans photonics applications in biomedical engineering, including Raman spectroscopy for extracellular vesicle analysis, metasurface-based optical trapping, and waveguide sensor development. Recent publications highlight innovations in dielectric nanotweezers, BIC-supported trapping systems, and self-supervised learning for spectral analysis. Collaborators include Md Rabiul Hasan, Mathias Novik Jensen, and Balpreet Singh Ahluwalia, with work featured in Nature Photonics , Optics Express , and ACS Applied Optical Materials . His team explores optical manipulation of biological nanoparticles, methane detection via cryptophane-cladded interferometers, and crystalline structure quality assessment using near-infrared tomography. As department head, Hellesø coordinates research in integrated optics and photonics. His laboratory in Teknologibygget Tromsø 3.060 focuses on photonic chip technologies for particle trapping and spectroscopic analysis. Current projects include optimizing metasurface sensitivity, developing low-noise Raman systems, and advancing on-chip methane sensors.

• Computer Vision
• Image Analysis
• Machine Learning
• +۱ مورد دیگر

Puneet Sharma is an Associate Professor at the Department of Automation and Process Technology, UiT The Arctic University of Norway. His primary research areas include computer vision, image analysis, machine learning, and wearables technology, with active contributions to atmospheric science data processing and maritime navigation AI applications. Current role: Associate Professor (Automation) Teaching: Industrial data communication (bachelor), Machine Vision (master) Research: Machine Learning Group member, focus on visual attention models, deep learning for PMSE segmentation, and wearable training systems Recent publications highlight his work in applying machine learning to Polar Mesospheric Summer Echoes (PMSE) analysis, noctilucent cloud classification, and biosignal-based maritime navigation studies. He participated in the Horizon 2020 WEKIT project for wearable-based industrial training.

• Logistics
• Operations Research
• Optimization Algorithms
• +۳ مورد دیگر

Anna Konovalenko is a Research Fellow in the Faculty of Logistics at Molde University College - Specialized University in Logistics (HiMolde). Her research focuses on optimizing logistics systems using advanced computational methods, particularly machine learning and deep reinforcement learning. She is affiliated with the ABC-AI research group (Applied, Basic, and Conscientious Artificial Intelligence). Her work addresses challenges in vehicle routing problems, including hidden constraints detection and dynamic optimization. Recent publications highlight contributions to combining machine learning with traditional operations research techniques to improve logistics efficiency.

• 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.

• Urban regeneration
• sustainable development
• urban morphology
• +۵ مورد دیگر

Assistant Professor Remco Elric de Koning works at the Department of Civil Engineering and Environmental Sciences at Western Norway University of Applied Sciences (HVL). His research focuses on Space Syntax , urban morphology , and GIS-based sustainable urban development , with particular emphasis on public space dynamics in coastal cities. His academic contributions include studies on spatial configurations and human behavior in urban environments. Recent publications analyze: Waterfront public squares in Bergen GIS applications for urban densification Algorithmic analysis of spatial relationships International space syntax methodologies He contributes to the SPACERGY research group, exploring spatial syntax applications in Norway, and teaches courses including: BYG130 Urban Design MOA252 Sustainability and land use planning BYG350 Bachelor Thesis (Spring 2026)

• Robotic Vision
• Autonomous Vehicles
• Computer Vision
• +۷ مورد دیگر

Dr. Annette Stahl is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). She is an Onsager Fellow and leads the Robot Vision Group, overseeing the AILARON project funded by the Research Council of Norway. Her roles include affiliation with NTNU AMOS and the SFI AutoShip Centre for Research-based Innovation. Stahl holds a PhD in Applied Mathematics (Computer Vision) from Heidelberg University. Affiliations: NTNU AMOS, SFI AutoShip, and AILARON project leadership Research Focus: Robotic vision, autonomous systems, underwater exploration, and aquaculture monitoring Education: PhD in Applied Mathematics (Computer Vision) – Heidelberg University Research Interests: Her work spans robotic and computer vision, control theory, autonomous vehicles, mathematical image analysis, and machine learning applications in aquaculture and maritime systems. Key focus areas include underwater SLAM, sensor fusion for autonomous ships, and plankton detection using AI. Articles: Recent publications emphasize underwater robotic systems, sensor fusion for autonomous navigation, and AI-driven aquaculture monitoring. Key themes include 3D reconstruction, visual SLAM, and maritime tracking algorithms. Grants & Projects: AILARON (FRINATEK/IKTPLUSS) AUTOSIGHT (IKTPLUSS) AROS (IKTPLUSS) SFI AutoShip (SFI) Advising: Main supervisor for 10+ PhD candidates (e.g., Trym Nygård, Mauhing Yip) Co-advisor for projects like INDISAL (fish identification) Labs/Teams: Robot Vision Group at NTNU Collaborations with SINTEF Ocean and Zebop AS

• Statistics
• Data Science
• Bayesian Analysis
• +۳ مورد دیگر

Gudmund Horn Hermansen is an Associate Professor at the University of Oslo, affiliated with the Department of Mathematics within the Faculty of Mathematics and Natural Sciences. His research focuses on advanced statistical methodologies, including Bayesian analysis, time series modeling, and applications in fields such as conflict dynamics, neuroscience, and environmental science. He is a member of the Statistics and Data Science research group and collaborates with interdisciplinary teams on projects involving uncertainty quantification and statistical inference. Key research interests include change-point analysis, hidden Markov models, astrocytic calcium signaling in Alzheimer’s research, and probabilistic forecasting. His work bridges theoretical statistics with practical applications, such as analyzing democratization processes and reservoir parameter interactions. Hermansen has contributed to over 30 peer-reviewed publications, emphasizing methodological innovations and interdisciplinary collaborations. Notable publications include studies on Bayesian hidden Markov models in conflict research, astrocytic signaling mechanisms in sleep regulation, and statistical frameworks for temporal heterogeneity analysis. He maintains an active role in academic service, including editorial contributions and conference participation.

• Smart Wireless Systems
• Internet of Things (IoT)
• Information Security
• +۸ مورد دیگر

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

Fredrik Manneمشاهده پروفایل

استاد

• Parallel Computing
• Distributed Computing
• Graph Algorithms
• +۳ مورد دیگر

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

Omid Vakili Ebrahimiمشاهده پروفایل

پژوهشگر

• Clinical Psychology
• Psychiatric Epidemiology
• Methodology
• +۸ مورد دیگر

Omid Vakili Ebrahimi is a Researcher at the Department of Psychology, University of Oslo, and affiliated with PROMENTA and Modum Bad Psychiatric Hospital. He specializes in clinical psychology, psychiatric epidemiology, and complex systems modeling of mental health. Education: Cand.psychol. (University of Bergen, 2019), with visiting scholarships at Oxford, Amsterdam, Berkeley, Boston, and Hong Kong Key research areas: Mental health during pandemics, individual differences, network analysis, precision psychiatry, and integration of idiographic/nomothetic perspectives His work applies complex systems approaches to understand depression/anxiety dynamics during the COVID-19 pandemic, focusing on resilience, transdiagnostic mechanisms, and behavioral adherence to mitigation protocols. He has pioneered network models for mental health symptom interactions and developed scalable interventions. Recent publications (2024-2025) demonstrate strong trends in: Network analysis of mental health symptom dynamics Vaccination hesitancy modeling Pandemic-related psychological distress Cross-national mental health comparisons Digital well-being research Integration of computational methods in psychiatry Scientific recognition includes: International Council of Psychologists Early Career Award (2022) ABCT Mentorship Award (2022) Department of Psychology Research Dissemination Award (2020) Section Editor for The Great Norwegian Encyclopedia (2021-present) As a graduate student in the Norwegian Double Ph.D. Program, he investigated complex systems approaches to mental health disorders under advisors Sverre Urnes Johnson, Asle Hoffart, Ole Andre Solbakken, and Daniel J. Bauer. He teaches advanced psychopathology assessment and research communication.