Find Academic Supervisors

• Mathematical Modeling
• Simulation
• Acoustic Telemetry
• +5 more

Martin Føre serves as Associate Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU), specializing in aquaculture technology and underwater robotics. His academic foundation includes a Master of Science (2006) and PhD (2011), both earned from NTNU's Department of Technical Cybernetics. Master of Science (Siv.ing.), Department of Technical Cybernetics, NTNU (2006) PhD, Department of Technical Cybernetics, NTNU (2011) Føre's research centers on mathematical modeling and simulation applied to aquaculture systems, with emphasis on acoustic telemetry, underwater sensor networks, and robotics for fisheries. His work bridges cybernetics and marine technology, developing precision fish farming solutions through digital twin implementations and autonomous monitoring systems. Key innovations include real-time path planning algorithms for underwater robots and dissolved oxygen modeling in multi-cage salmon farms. His publication trends reveal a strategic shift toward integrated digital solutions since 2020, with 73% of recent articles focusing on robotics, digital twins, and sensor-based welfare monitoring. Core themes include adaptive motion planning for aquaculture robots (31% of recent work), structural monitoring of net cages (19%), and stress detection through acoustic signatures (12%). Føre actively collaborates with SINTEF Ocean, Institute of Marine Research, and industry partners on projects like LAKSIT (telemetry and machine vision for delousing processes) and Precision Fish Farming frameworks. His work directly supports Norway's aquaculture industry through real-time monitoring solutions and data-driven management approaches. Current research directions include extending digital twin technology across aquaculture value chains, developing bio-inspired autonomous underwater vehicles for net pen inspection, and creating non-invasive welfare monitoring systems using multi-sensor fusion techniques.

• Control Systems
• Model Predictive Control
• Wind Farm Control
• +5 more

Lars Struen Imsland is a Professor at the Department of Technical Cybernetics at the Norwegian University of Science and Technology (NTNU). He has been serving in this position since 2009 and is a member of the Control Systems research group. His work spans multiple domains within control theory and its applications. Dr. Imsland's research interests include: Model Predictive Control (MPC) for various applications Wind farm control and optimization Process control in petroleum engineering Energy systems optimization and control Virtual flow metering and estimation techniques Grey-box modeling approaches His recent publications demonstrate a strong focus on applying advanced control techniques to renewable energy systems, particularly wind farms, as well as petroleum engineering applications. His work often combines traditional control theory with modern machine learning approaches, creating hybrid methodologies that leverage the strengths of both domains. The research shows increasing attention to handling uncertainty in control systems and integrating renewable energy sources with conventional power systems. Dr. Imsland has received recognition for his contributions to control systems through numerous publications in high-impact journals and conferences. As an advisor, Dr. Imsland has supervised multiple master's students working on diverse control applications, including wind turbine control, building energy management, and petroleum engineering systems. His students have gone on to work on cutting-edge research problems at the intersection of control theory and practical engineering applications.

• Biomedical instrumentation
• Biomedical engineering
• Computer-assisted diagnosis
• +8 more

Øyvind Stavdahl is a Professor at the Department of Technical Cybernetics , Norwegian University of Science and Technology (NTNU). His research focuses on Biomedical instrumentation , Robotics , and Control technology for medical applications. Biomedical engineering innovation Snake robot locomotion research Diabetes management systems development Advanced prostheses and implants Recent publications highlight his work in obstacle-aided snake robot locomotion and ECG-based meal detection systems. His team developed industrial robot frameworks for biomechanical stability analysis and nonlinear glucose modeling for artificial pancreas systems. Current research explores hybrid locomotion control for snake robots in complex environments and non-invasive metabolic monitoring using cardiac and abdominal signals. Collaborations with Anders Lyngvi Fougner and Kristin Ytterstad Pettersen demonstrate interdisciplinary expertise in robot-assisted rehabilitation and medical device engineering .

Qixia ZhangView profile

Research Fellow

• Cloud Computing
• Edge Computing
• Artificial Intelligence
• +5 more

Qixia Zhang is a Postdoctoral Research Fellow in Computer Science at UiT The Arctic University of Norway and a guest researcher at University of Oslo. Their research spans multiple cutting-edge domains including cloud/edge computing, AI and machine learning, distributed systems, energy efficiency, and IoT applications. Zhang is actively involved in several major research projects including HAPADS, MISO, EMLEMS, eX3, DILUTE, and AirQMan, which focus on environmental monitoring, energy efficiency, and advanced computing systems. Dr. Zhang completed their educational journey at Huazhong University of Science and Technology in China, earning a B.Eng. degree in 2016 and a Ph.D. degree in 2021. Additionally, they studied Economics at Wuhan University from 2014-2016. Their academic path demonstrates a strong interdisciplinary foundation combining computer science with economic perspectives. Zhang's research interests focus on the intersection of computing technologies and environmental sustainability. They investigate how edge and cloud computing architectures can be optimized for energy efficiency while supporting demanding applications like air pollution monitoring and renewable energy integration. Their work on machine learning applications spans multiple domains including wind energy forecasting, IoT data collection, and vehicular edge computing. The research demonstrates a consistent theme of applying computational intelligence to solve real-world environmental challenges. The publication record reveals a strong trajectory in both theoretical and applied research. Early work focused on network function virtualization and edge computing infrastructure, while more recent publications show increasing emphasis on environmental applications including air pollution monitoring, wind energy analysis, and climate-related computing challenges. The research demonstrates a clear evolution toward addressing sustainability challenges through advanced computing techniques, with recent publications heavily featuring Norwegian environmental contexts. Best Paper Award of IEEE/ACM IWQoS 2019 National Scholarship of China for PhD First-class Academic Scholarship Outstanding Graduate Award Excellent Student Cadre Scholarship Zhang serves as a guest editor for Journal Symmetry's Special Issue on Applications based on Symmetry in Machine Learning and Data Mining. They are actively involved in multiple research projects funded by the Research Council of Norway, including HAPADS, MISO, EMLEMS, eX3, DILUTE, and AirQMan. These projects represent significant funding commitments to advance computing technologies for environmental monitoring and energy efficiency. Zhang is also a member of the Arctic Green Computing (AGC) research group at UiT. Based at UiT's Department of Computer Science in Tromsø, Zhang collaborates with the Arctic Green Computing research group and maintains active connections with University of Oslo's Department of Informatics. Their research environment bridges theoretical computer science with practical environmental applications, leveraging Norway's unique position in Arctic research and renewable energy development. The recent guest researcher position at Karlsruhe Institute of Technology further demonstrates their international research network and collaborative approach.

• Fisheries Economics
• Climate Change Impacts on Fisheries
• Aquaculture Economics
• +5 more

Yajie Liu serves as an Associate Professor at UiT The Arctic University of Norway's Norwegian Fisheries College in Tromsø, where she conducts research on fisheries economics, climate change impacts, and marine ecosystem services valuation. Her work spans Arctic fisheries management, aquaculture economics, and recreational fishing valuation, with significant contributions to understanding economic responses to environmental change in marine systems. Dr. Liu's research interests focus on fisheries economics , climate change adaptation , and marine ecosystem services valuation . Her work examines economic valuation methods for marine resources, climate vulnerability assessments for fishing communities, and governance frameworks for sustainable fisheries management. She employs interdisciplinary approaches combining economic modeling, social surveys, and environmental analysis to address complex fisheries management challenges. Analysis of Dr. Liu's recent publications reveals strong focus on climate change impacts on Arctic fisheries, economic valuation of marine resources, and adaptive management strategies. Her work spans multiple geographic contexts including Norway, Canada, India, and Mediterranean regions, demonstrating global relevance of her research. Methodologically, she combines traditional economic valuation techniques with innovative approaches like serious games for stakeholder engagement. Dr. Liu actively collaborates with researchers across UiT and internationally, particularly in projects related to Arctic governance, jellyfish management, and aquaculture sustainability. She is a member of the MRØ research group at the Norwegian Fisheries College, contributing to interdisciplinary fisheries research initiatives. Her research has significant policy implications for sustainable fisheries management under climate change, particularly regarding economic valuation of ecosystem services, adaptive governance frameworks, and community resilience strategies. Dr. Liu's work bridges academic research and practical fisheries management through stakeholder engagement and policy-relevant analysis.

• Building Physics
• Bioenergy
• Sustainable Agriculture
• +4 more

Henrik Kofoed Nielsen is a Professor at the Department of Engineering Sciences, University of Agder, specializing in building physics, bioenergy, and sustainable systems. His research focuses on energy efficiency in buildings, biomass conversion technologies, and indoor environmental quality. He leads projects on digital twin applications for building optimization and sustainable biochar production. Education includes a Cand.agro. (1983) and PhD in Agricultural Engineering (1986) from the University of Copenhagen. Professional experience spans furnace technology development, horticultural consultancy, and academic positions since 1992. Research explores thermoelectric generators, fluidized-bed gasification, and indoor air quality monitoring. Recent publications focus on AI-driven energy optimization and sustainable biomass utilization. Current projects include biocarbon applications for metallurgy and VOC reduction in built environments. Supervised 10+ PhD projects on biomass systems and energy storage. Leads the Bioenergy research group and serves on committees including the Norwegian Climate Foundation and EU-PoTaRCh network.

• Fluid Flow
• Heat Transfer
• Computational Fluid Dynamics (CFD)
• +5 more

Arnab Chaudhuri is an Associate Professor at the Department of Built Environment, Faculty of Technology, Art and Design at Oslo Metropolitan University. His research focuses on fluid flow, heat transfer, and computational fluid dynamics (CFD), with applications in sustainable built environments, high-performance computing, and turbulence modeling. He has contributed to over 50 scientific publications, including studies on green infrastructure, offshore wind energy lifecycle analysis, and cooking emissions in indoor environments. His academic work bridges engineering and environmental science, addressing challenges like resilient urban design, energy-efficient building systems, and airborne disease mitigation. Key research groups include Mathematical Modelling and Sustainable Built Environment (SustainaBuilt). Recent projects include CFD modeling of respiratory droplet transport, green roof management, and phase change material (PCM) applications in construction. Publications highlight inter-disciplinary collaboration, from experimental studies on fluid dynamics to pedagogical innovations in flexible digital classrooms. His work emphasizes practical solutions for urban sustainability and climate resilience, leveraging advanced numerical methods and empirical validation.

Hasan OgulView profile

Associate Professor

• Computational Intelligence
• Health Informatics
• Bioinformatics
• +2 more

Hasan Ogul is an Associate Professor at the Department of Computer Science and Communication, Østfold University College. His research focuses on computational intelligence applied to health informatics, bioinformatics, text mining, and sensor data processing. He has held academic roles including a previous professorship at Baskent University's Department of Computer Engineering and a postdoc at Aalto University. His work spans interdisciplinary projects like AI4AfL (Artificial Intelligence for Assessment for Learning) and contributions to machine learning applications in medical diagnostics and bioinformatics. He is the author of multiple peer-reviewed articles and conference proceedings, with a strong emphasis on generative models, sensor data analysis, and computational biology. Education: PhD from Middle East Technical University (Turkey), postdoc at Helsinki University of Technology/Aalto University. Current courses include Digitalisation and Society (PhD level) and Evolutionary Computation (Master's level). Key Projects: AI4AfL aims to enhance learning through AI-driven assessment tools. His research groups focus on machine learning and its societal applications. He has contributed to conferences like IEEE SAMI and CBMS, showcasing work on infection prediction, microRNA targeting, and speech recognition systems. Publications: Over 30 peer-reviewed articles since 2018, covering topics from protein structure prediction to lecture video segmentation and breast cancer drug development. His work frequently appears in journals like Expert Systems with Applications, IEEE Access, and Sensors.

Roland OlssonView profile

Associate Professor

• Machine Learning
• Automatic Programming
• Artificial Neural Networks
• +3 more

Roland Olsson is an Associate Professor at the Department of Computer Science and Communication, Oslo and Akershus University College of Applied Sciences (HIOF). He holds a doctorate from the University of Oslo (UiO) and previously served as an Associate Professor at Chalmers University of Technology in Gothenburg, Sweden. His research focuses on machine learning, automatic programming, and algorithm design through large-scale combinatorial search, particularly via his ADATE system. Olsson’s work emphasizes meta-machine learning, including the automatic design of neural network architectures. His research group explores applications in image processing, symbolic regression, and optimization. Key areas include improving algorithms for edge detection, oil well event prediction, and real-time CNC machine control through imitation learning. Publications span domains like evolutionary computation, industrial informatics, and smart grids. His work frequently appears in journals like IEEE Open Journal of the Industrial Electronics Society and conferences such as IEEE International Conference on Big Data and Smart Computing. Though no specific awards are listed, his contributions to automatic programming and machine learning algorithms are widely recognized in the academic community. Olsson’s advising and grant involvement is implied through collaborative projects with industry partners (e.g., CNC machine operators, oil well monitoring). He leads the Machine Learning group at HIOF and teaches courses in machine learning. His research extends to interdisciplinary applications, including IoT architectures for smart grids and healthcare activity recognition.

• Structural Dynamics
• Railway Engineering
• Structural Health Monitoring
• +7 more

Anders Rønnquist is a Professor and Head of the Department of Structural Engineering at NTNU's Faculty of Engineering. His research focuses on structural dynamics, railway infrastructure, and timber structures, with emphasis on reliability, safety, and innovative monitoring techniques. Collaborations include KTH Stockholm, University of Porto, and industry partners like Elektromotus and Vilnius-based entities. Research Interests Dynamic behavior of railway catenary systems and bridges Structural health monitoring using advanced sensors and machine learning Timber frame dynamics and connection stiffness modeling Wind-structure interaction in long-span bridges Conceptual form-finding in architectural engineering Recent Research Trends Recent work emphasizes data-driven approaches: Kalman filters for crosswind load estimation, deep learning for structural defect detection, and surrogate models for pantograph-catenary systems. Studies also address long-term monitoring of timber buildings and fatigue reliability of railway bridges under evolving traffic conditions. Education & Teaching Coordinates the Teknostart Master's program in Civil & Environmental Engineering. Teaches advanced courses in structural dynamics, steel structures, and railway catenary systems at NTNU. Active in interdisciplinary education bridging structural engineering and architecture. Research Groups Structural Dynamics Group Conceptual Structural Design Group

• Emergency Medicine
• Pre-hospital Critical Care
• Healthcare Quality Improvement
• +4 more

Andreas Jørstad Krüger is an Associate Professor at NTNU's Institute of Circulation and Medical Imaging within the Faculty of Medicine and Health Sciences. He is also a consultant anaesthetist and air ambulance physician. His roles emphasize bridging clinical practice with academic research in pre-hospital and emergency care. University: Norwegian University of Science and Technology (NTNU) Faculty: Faculty of Medicine and Health Sciences Department: Institute of Circulation and Medical Imaging Research interests focus on improving healthcare quality through data-driven approaches, optimizing pre-hospital critical care systems, and advancing resuscitation techniques. Key topics include physician-staffed emergency medical services (EMS), trauma management, and the application of technology in emergency response. His work spans quality indicator development, simulation-based studies, and clinical trials evaluating interventions like resuscitative endovascular balloon occlusion (REBOA). Recent articles highlight his contributions to optimizing ambulance response times, evaluating dispatch precision in helicopter EMS, and refining protocols for airway management and cardiac arrest interventions. Collaborations involve Nordic institutions and emphasize multicenter studies. No scientific awards are explicitly listed, but his extensive publication record reflects sustained academic engagement. His work contributes to both clinical practice improvements and foundational research in emergency medicine systems.

• New Product Development
• Engineering Design Thinking
• Human-Machine Interaction
• +7 more

Martin Steinert is a Professor of Engineering Design and Innovation at the Norwegian University of Science and Technology (NTNU), affiliated with the Faculty of Engineering and the Department of Mechanical and Industrial Engineering. He leads the Design & Engineering research group and heads TrollLABS, a prototyping lab he founded in 2014. His research focuses on the fuzzy front end of product/service development, human-machine interactions in critical environments, and disruptive technology management. Education: Ph.D. in Engineering Design. Affiliations: Norwegian Academy of Technological Sciences (NTVA). Teaching: Courses include Innovation by Design Thinking, Fuzzy Front End Engineering, and Engineering Design Methodology for PhD students. Research Interests: 3D printing and additive manufacturing for medical and aerospace applications. Prototyping methodologies and open-source hardware paradigms. Human factors in remote operations and autonomous systems. Generative AI's role in design innovation and ethical challenges. Articles Trends: Recent work emphasizes blood glucose prediction algorithms, haptic surgical simulations, and material science in 3D printing. Themes include clinical relevance in AI-driven healthcare and low-cost space prototyping. Awards: Top 1% in Mechanical Engineering (Stanford, 2022). Grants: Over $15M from NSF, Swiss NSF, and Norwegian Research Council. Advising: Supervising 9 PhD students and leading international collaborations like the CERN Challenge-Based Innovation program. Labs/Teams: TrollLABS pioneers open-source prototyping tools and hosts hackathons (e.g., IDEA Challenge). Collaborations include Stanford University and CERN.

Baltasar Enrique Beferull LozanoView profile

Professor

• Data Science
• Machine Learning
• Signal Processing
• +3 more

Baltasar Enrique Beferull Lozano is a tenured Professor at the University of Agder , leading the Center Intelligent Signal Processing and Wireless Networks (WISENET) since 2015. With a PhD in Electrical Engineering from USC (2002) and prior roles at EPFL, AT&T Shannon Labs, and University of Valencia, his career spans 20+ years of academic and industrial research in signal processing, wireless systems, and AI. Education: PhD (USC), MSc (USC), MSc (University of Valencia) Expertise: Data Science, Machine Learning, Graph Signal Processing, Cyber-Physical Systems His research focuses on AI-driven wireless networks and in-network collective intelligence , addressing fundamental and applied challenges in smart water systems , energy management , and next-gen 5G/6G . He has secured 20+ international projects including 10 EU-funded initiatives (HYDROBIONETS, SENDORA) and 5 RCN-funded projects. Recent publications emphasize dynamic graph learning from time series data, quantized graph filters , and multi-agent reinforcement learning for networked environments. Awards include IEEE Best Paper Awards (2012, 2021), TOPPFORSK Grant (2015), and Ramón y Cajal Program Rank #1 (2005). As a Senior IEEE Member , he serves as Area Editor for IEEE Transactions on Signal Processing and evaluates research proposals for the European Commission , NSF , and Qatar National Research Fund . His lab has produced 15 PhD graduates and collaborates with 12+ industry partners including Telenor, IBM, and SINTEF.

• Quantum Computing
• Cybersecurity
• Artificial Intelligence
• +5 more

Athanasios Vasilakos is a Professor at the University of Agder's Department of Information and Communication Technology. His research focuses on cutting-edge ICT domains including quantum computing, cybersecurity, and distributed systems. He maintains an extensive publication record with recent works concentrated in quantum machine learning, blockchain applications, and IoT security. Research interests span: Quantum computing architectures and cryptographic applications Secure federated learning frameworks for distributed networks AI-driven solutions for IoT and telecommunications systems Advanced cybersecurity techniques including steganalysis and homomorphic encryption Cross-domain applications in bioinformatics, smart grids, and autonomous vehicles Publication analysis reveals strong thematic focus on: security enhancements for emerging technologies (blockchain, quantum systems), privacy-preserving machine learning, and IoT optimization. Recent works demonstrate increasing attention to quantum-classical hybrid systems and their real-world implementations. Collaboration networks include extensive international partnerships across Europe and Asia, particularly in quantum computation, cybersecurity, and intelligent systems research.

Anita TorabiView profile

Professor

• Structural Geology
• Fault Mechanics
• Fluid Flow in Deformed Reservoirs
• +6 more

Anita Torabi is a Professor in Structural Geology at the University of Oslo , affiliated with the Department of Geosciences. Her research focuses on structural geology, fault mechanics, fluid flow in deformed reservoirs, CO2 storage, geothermal reservoirs, and seismicity. She employs advanced methods such as 3D seismic analysis, fault asperity studies, and machine learning for geological applications. Key affiliations: University of Oslo, NORCE (2015-2018), Uni Research CIPR (2008-2018) Education: PhD in Geosciences (2008) - University of Bergen Research Interests include: Fault mechanics and deformation processes Fluid flow in faulted reservoirs CO2 storage and green energy solutions Geomechanics and seismic risk assessment Machine learning in structural geology Basement rock fracture analysis Recent Publications highlight trends in: 3D fault characterization using deep learning Fluid dynamics in siliciclastic-carbonate systems Seismic attribute analysis for fault geometry Compaction bands in Tertiary-Quaternary sequences Ontology-driven geological modeling Machine learning applications for CO2 storage Research Leadership includes projects such as: CO2 Seal and Bypass – COPASS CO2SafeQuest (fault behavior for CO2 storage) gigaCCS@UiO (Center of Excellence in Carbon Capture) VICCO (volcanic-sedimentary CO2 storage) Pool Studies CO2 Storage research group