جست‌وجوی استاد و پژوهشگر

• Safe Reinforcement Learning
• Data-Driven Model Predictive Control
• Artificial Intelligence
• +۷ مورد دیگر

Sebastien Nicolas Gros is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His research focuses on safe reinforcement learning (RL) and data-driven model predictive control (MPC), with applications in energy systems, biomedical engineering, and autonomous vehicles. Institution: Norwegian University of Science and Technology Department: Engineering Cybernetics His work emphasizes AI-driven optimization for domestic energy storage, battery integration, and smart building management. Collaborations include Equinor, DNV, Kongsberg, Volvo, and CorPower Ocean. Key themes in his publications include: Control theory for renewable energy systems (wave energy converters, buildings) Biomedical applications (artificial pancreas, glucose monitoring) Transportation systems (electric vehicles, autonomous ships) Machine learning integration with physical models He supervises 6 PhD students and co-supervises projects on multi-rotor wind turbines and industrial PhD collaborations. The articles demonstrate a convergence of RL, MPC, and uncertainty quantification across energy, biomedical, and transportation domains.

• Artificial Intelligence
• Machine Learning
• Medical Technology
• +۷ مورد دیگر

Anis Yazidi is a Professor at Oslo Metropolitan University, affiliated with the Faculty of Technology, Art and Design and the Department of Information Technology. His research focuses on Artificial Intelligence, Machine Learning, Medical Technology, and IoT Security, with a particular emphasis on Applications of AI in Healthcare, EEG Signal Processing, and Digital Transformation. Active research projects include AI Mind (dementia diagnostics), Glycopathology in dry eyes, and Pain and mental distress analysis Completed projects: Digital hate speech analysis, AI in reproductive technology, Nano-antibiotics development His recent publications (2023-2025) demonstrate expertise in: Tsetlin Automaton algorithms for concept learning EEG classification using visibility graphs and vision transformers AI ethics frameworks for medical practice Deepfake detection methodologies Collaborative work spans institutions in Norway, Czech Republic, and international AI research communities.

Hans Jonas Fossum Moenمشاهده پروفایل

دانشیار

• Autonomous Systems
• Sensor Technologies
• Robotics
• +۳ مورد دیگر

Hans Jonas Fossum Moen is an Associate Professor with a 20% appointment at the Department of Technology Systems, University of Oslo (UiO), and holds a 100% position as a researcher at the Norwegian Defence Research Establishment (FFI). His primary affiliation is with the Section for Autonomous Systems and Sensor Technologies. He is based at the Kjeller campus, with a visiting address at Gunnar Randers Road 19 and a postal address at Postboks 70. His research focuses on advancing autonomous systems and sensor technologies, particularly in the domains of swarm robotics, multi-agent coordination, and optimization algorithms. Key areas include UAV navigation, distributed localization in IoT networks, radar detection enhancement, and adaptive control systems for multi-functional swarms. He emphasizes the integration of biological principles into robotic systems, as evidenced by his participation in the ICRA 2018 Workshop on Swarms. His publications consistently highlight contributions to swarm intelligence, with a focus on improving data quality and efficiency in robotics applications. He has collaborated extensively with colleagues such as Kyrre Glette, Oleg Yakimenko, and Jan Dyre Bjerknes, exploring topics ranging from task allocation in multi-agent systems to evolutionary algorithms for filter optimization. His work bridges theoretical computer science with practical engineering challenges in autonomous systems. No scientific awards have been explicitly mentioned in the provided texts. Moen’s advising and grants narrative indicates no listed advisees or active grant projects, though his 20% UiO position suggests potential involvement in academic supervision. His primary research activities are embedded within FFI and the Autonomous Systems section at UiO, contributing to interdisciplinary efforts in sensor technologies and robotic systems.

Hanna-Riikka Roineمشاهده پروفایل

دانشیار

• Digital Culture
• Narrative Theory
• Game Studies
• +۷ مورد دیگر

Hanna-Riikka Roine is an Associate Professor in Digital Culture at the University of Bergen, affiliated with the Department of Linguistic, Literary and Aesthetic Studies and the Center for Digital Narrative. Her research bridges digital media, narrative theory, and speculative fiction, with a focus on games, imagination, and democratic discourse. Her research interests include: Digital culture and computational media Narrative theory and worldbuilding Game studies, particularly narrative agency in video games Speculative fiction as a tool for imagining futures Transmediality and digital storytelling The role of fiction in democratic imagination Her recent publications explore narrative forms in social media, romance mechanics in games like Dragon Age , and the limits of narrativizing environmental systems. She investigates how digital technologies entangle with human imagination, especially in speculative contexts. Notable research projects include: CO-SPEC (Academy of Finland, 2020–2023): On human-computer entanglement in speculation Imagine, Democracy! (Kone Foundation, 2023–2026): On narrative fiction as a democratic imagination tool in Finland She has co-edited special issues and books on digital environments, games, and speculative fiction, demonstrating leadership in interdisciplinary scholarship. While no formal scientific awards are listed, her funded projects and editorial roles signify academic recognition. She has collaborated with researchers such as Laura Piippo, Esko Suoranta, and Susanna Lindberg, contributing to a vibrant research network. She has also developed innovative pedagogical methods, such as using Twine game jams for assessment in higher education. Her work is situated within a strong digital humanities framework, connecting literary theory with contemporary media and societal challenges.

• Sports Cardiology
• Cardiovascular Incidents in Athletes
• Paralympic Athlete Health
• +۴ مورد دیگر

Hilde Moseby Berge serves as an Associate Professor in the Department of Sports Medicine at the Norwegian School of Sport Sciences. She is actively engaged in research and clinical work focused on sports medicine, particularly in the areas of sports cardiology and Para athlete health. Her contact information indicates ongoing institutional affiliation with direct lines for professional communication. Dr. Berge's research interests center on cardiovascular health in athletes, with particular emphasis on sudden cardiac arrest prevention, ECG interpretation in sports settings, and health monitoring systems for elite athletes. Her work spans both Olympic and Paralympic populations, with significant contributions to understanding injury and illness patterns among these athletes. She has developed expertise in periodic health evaluations and has led initiatives to improve cardiac emergency response in sports settings, particularly through projects like ParaFRISK and YoungParaFRISK. Analysis of her publication record reveals consistent focus on cardiovascular incidents in sports, particularly examining the limitations of cardiac screening programs and the prevalence of cardiac events even among athletes with previously negative screening results. Her more recent work has expanded to include comprehensive health monitoring systems for Paralympic athletes and community-based interventions to improve survival from cardiac events in sports settings. Her research bridges clinical practice with epidemiological investigation, contributing significantly to evidence-based approaches in sports medicine. Dr. Berge maintains active collaborations with key researchers in sports medicine including Kathrin Steffen, Benjamin Clarsen, and Roald Bahr. Her work has contributed to national health monitoring programs for Norwegian Olympic and Paralympic athletes, with longitudinal data collection spanning multiple Games cycles. She appears to be involved in practical implementation of health services during major sporting events, as indicated by her publications related to preparation for the Tokyo and Paris Games.

• Control Systems
• Robotics
• Marine Robotics
• +۳ مورد دیگر

Kristin Y. Pettersen is a Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU), and a Professor II at the Norwegian Defence Research Institute (FFI). She is a co-founder of Eelume AS, a company specializing in underwater robotics solutions. Education: Civil Engineering and PhD in Technical Cybernetics from NTNU Her research focuses on advanced control systems for marine and underwater vehicles, particularly snake robots and autonomous underwater vehicles (AUVs). Key areas include formation control, path following, adaptive guidance algorithms, and safety-critical control in dynamic environments. Recent work explores machine learning integration and energy-shaping techniques for robust locomotion. Publications highlight trends in Model Predictive Control (MPC) , Collision Avoidance , and Task-Priority Operational Space Control for redundant and underactuated systems. Her work bridges theoretical control theory with practical applications in marine robotics, including autonomous inspections and cooperative transport. Labs/Teams: Collaborates with NTNU's Faculty of Information Technology and Electrical Engineering and co-founded Eelume AS, advancing subsea robotic manipulation technologies.

• Internet of Things
• Fog Computing
• Edge Computing
• +۹ مورد دیگر

Amirhosein Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, dependability, mobility and data-intensiveness of distributed systems for emerging computing technologies including Internet of Things (IoT), Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). Dr. Taherkordi received his Ph.D. from the Informatics Department at the University of Oslo under the supervision of Prof. Frank Eliassen, with his thesis titled "Programming Wireless Sensor Networks: From Static to Adaptive Models." He holds an M.Sc. in Information Technology Engineering (Software Engineering) from University of Science and Technology and a B.Sc. in Computer Engineering from Sharif University of Technology. His research spans multiple domains of distributed systems with emphasis on practical applications. He investigates energy efficiency in wireless sensor networks, communication optimization in IoT systems, and adaptive resource allocation in edge computing environments. His work addresses critical challenges in network traffic classification, federated learning for vehicular networks, and data processing across heterogeneous platforms. Analysis of his recent publications reveals a strong trajectory toward communication-efficient federated learning techniques for vehicular networks, energy-aware protocols for IoT data collection, and advanced machine learning approaches for network traffic analysis. His research consistently focuses on optimizing resource usage while maintaining system performance and privacy in distributed architectures. Dr. Taherkordi actively contributes to several research initiatives including the CPS Lab at UiO for Cyber Physical Systems, DILUTE: Fluid Service Abstraction for Large-Scale Cloud IoT Systems, and the Gemini Centre on IoT at UiO. His work bridges theoretical advances with practical implementations in transportation systems, environmental monitoring, and industrial automation.

Marianne Zeyringerمشاهده پروفایل

دانشیار

• Renewable Energy Systems
• Power System Modeling
• Climate Science
• +۳ مورد دیگر

Marianne Zeyringer is an Associate Professor in Renewable Energy Systems at the Department of Technology Systems (ITS), University of Oslo. She leads the Energy Systems Modelling Group and co-leads the SPATUS Thematic Research Group. Her research focuses on designing net-zero energy systems with high shares of renewables, emphasizing interdisciplinary approaches integrating social sciences and climate science. She coordinates the UiO:Energy Convergence Environment EMPOWER and leads initiatives like the dScience Community Forum. Previously, she held roles at UCL Energy Institute, the European Commission Joint Research Centre, and Lawrence Berkeley National Laboratory. She teaches Energy Markets and Regulation and supervises multiple PhD students exploring topics like hydrogen storage, social factors in energy models, and climate risk. She actively contributes to editorial boards and international projects, including the NextGen Energy Climate Group and the ERC grant reFUEL. Her work emphasizes climate-resilient energy systems, spatial-temporal modeling, and equity considerations. Notable contributions include highRES-Europe, a high-resolution electricity system model, and studies on wind energy variability and policy impacts.

• Cyber-Physical Systems
• Distributed Systems
• Internet of Things (IoT)
• +۷ مورد دیگر

Loïc Guégan is an Associate Professor in the Department of Informatics at UiT The Arctic University of Norway, affiliated with the Faculty of Science and Technology. His work is centered on energy-efficient cyber-physical systems and distributed computing, particularly in extreme and constrained environments such as the Arctic. His research focuses on cyber-physical systems (CPS) , distributed data dissemination , IoT and edge computing , and energy-efficient protocols . He actively contributes to the development of the Distributed Arctic Observatory (DAO) project and has designed tools like the ESDS simulator for evaluating distributed systems in challenging conditions. His technical work includes power monitoring using single-board computers and the design of the LoRaLitE protocol for low-energy wireless communication. The recent publications highlight a consistent trend in data dissemination strategies , simulation frameworks , and energy optimization for IoT and edge systems deployed in remote, resource-limited settings. These studies often leverage real-world Arctic deployments and epidemic-style algorithms to ensure robustness and scalability. Loïc is a member of the Cyber Physical Systems (CPS) research group and contributes to projects including The IoT-to-Extreme-Edge Infrastructure and Sustainable Distributed Systems for Sustainable Research and Education . He teaches courses such as Parallel Programming and Operating Systems . His research is supported through institutional and project-based funding, though specific grants are not detailed in the text.

• Cryptographic Voting Systems
• Key Exchange Protocols
• Cloud Cryptography
• +۳ مورد دیگر

Kristian Gjøsteen is a Professor at the Department of Mathematical Sciences within the Norwegian University of Science and Technology (NTNU) . He actively contributes to the Algebra Group and specializes in cryptographic systems with a focus on electronic voting , security proofs , and privacy-enhancing technologies . Educational Background: MSc and PhD from NTNU Research Interests: His work spans cryptography , key exchange protocols , cloud security , and formal verification of security mechanisms. Particular emphasis is placed on coercion-resistant voting systems , lattice-based encryption , and blockchain privacy models . Article Trends: Recent publications demonstrate expertise in post-quantum cryptography , machine-checked security , and privacy-preserving voting architectures . Collaborative efforts explore hybrid cryptographic schemes , verifiable decryption , and mix-net implementations for secure elections.

Ajit Jhaمشاهده پروفایل

دانشیار

• Photonic sensing
• Robotics
• Machine Learning
• +۶ مورد دیگر

Ajit Jha is an Associate Professor at the Department of Engineering Sciences , University of Agder , Norway, with research expertise in photonic sensing, robotics, machine learning, and sensor fusion. His work bridges theoretical advancements with real-world applications in autonomous systems, industrial automation, and biomedical imaging. Research Areas: Photonic sensing, Robotics, Machine Learning, Computer Vision, Sensor Fusion, Mechatronics Recent Publications demonstrate innovative applications of deep learning to thermal imaging (gesture recognition), reinforcement learning for drone landing, and sensor fusion techniques for autonomous navigation. His interdisciplinary approach combines photonics, radar systems, and AI to solve complex engineering problems.

• Medical Ethics
• Clinical Ethics
• Doctor-Patient Communication
• +۴ مورد دیگر

Kari Milch Agledahl serves as an Associate Professor at UiT The Arctic University of Norway within the Faculty of Health Sciences, specifically affiliated with the Interdisciplinary district medical research group in Tromsø. Her academic work bridges medical practice and ethical considerations, focusing on the practical moral dimensions of clinical work. Dr. Agledahl's research interests center on medical ethics with particular emphasis on doctor-patient communication, operating room ethics, and cultural competence in healthcare settings. Her work critically examines how medical professionals navigate moral challenges in everyday practice, with special attention to existential aspects of patient care that often remain unaddressed despite surface-level politeness in clinical encounters. She employs qualitative methodologies, particularly video-recorded patient consultations, to investigate the 'black box' of medical ethics in real-world settings. Analysis of her publication record reveals a consistent research trajectory examining the moral conditions of medical work, with recent focus on consent processes, operating room ethics, and regional healthcare challenges in Northern Norway. Her work demonstrates how theoretical bioethical concepts manifest (or fail to manifest) in actual clinical practice, challenging common assumptions about patient autonomy and medical decision-making. Dr. Agledahl actively participates in research projects including 'Cultural competence among doctors in a culturally diverse region - better care for minority patients' and 'Operation room ethics. Investigating the black box of medical ethics,' indicating her commitment to addressing practical ethical challenges in diverse healthcare contexts.

Jaakko Timo Henrik Järviمشاهده پروفایل

استاد

• Generic Programming
• Programming Language Design
• Graphical User Interfaces (GUI)
• +۷ مورد دیگر

Jaakko Timo Henrik Järvi is a Professor in the Department of Informatics at the University of Bergen, Norway, with additional affiliations at the University of Turku, Finland. His research focuses on programming language design, generic programming, and human-computer interaction, particularly in GUI frameworks and software reuse. His research interests include generic programming, programming language design (especially the Magnolia language), high-performance computing, array programming, and GUI engineering. He emphasizes formal methods and algebraic specifications to build reusable and efficient software systems. His work bridges theoretical foundations with practical applications in software development and education. The recent publications highlight a strong trend in declarative GUI frameworks, multi-selection models, and generic programming. His work explores domain-specific languages for GUI structure manipulation, reusable selection semantics across platforms, and optimizing array computations using the Mathematics of Arrays. These efforts reflect a consistent focus on software abstraction, correctness, and reusability. Jaakko Järvi has supervised doctoral students, including Tetiana Yarygina, whose dissertation explored microservice security. While no specific grants are detailed, his work on VisAST was supported by the Research Council of Norway (Project 250683), indicating active external funding. He frequently collaborates with researchers like Magne Haveraaen, Knut Anders Stokke, and Sean Parent. He contributes to tools and frameworks such as the MultiselectJS library and the VisAST educational tool. These are outcomes of collaborative research teams focused on improving software development practices and computer science education.

Hallvard Moeمشاهده پروفایل

استاد

• Media and Democracy
• Public Sphere Theory
• Media Policy
• +۱۲ مورد دیگر

Hallvard Moe is a Professor in Media Studies at the Department of Information and Media Studies , University of Bergen (UiB) , Norway. He is a leading researcher in media and democracy, public service media, and digital media use, leading the Media Use Group and the ERC-funded PREPARE project. He actively teaches and supervises students in media-related fields. His research focuses on the democratic role of media, particularly how public service media adapt to digital platforms, the impact of algorithms and datafication, and citizens’ news use during crises like the pandemic and climate change. He has extensively studied Nordic media systems, social media in political campaigns, and media policy across Europe. The 15 most recent publications highlight a consistent research trajectory centered on public connection, media trust, and digital transformation. Key themes include crisis news consumption, algorithmic perception, media welfare state critique, and evolving citizen roles in datafied societies. Methodologically, he employs qualitative and mixed methods, including diaries and cross-national comparisons. ERC Consolidator Grant (2022) for the PREPARE project Member of the Norwegian Broadcasting Council Member of the Media Diversity Committee (2015–2017) He supervises master’s and PhD students and leads multiple research projects funded by the Research Council of Norway and the European Research Council. He is a frequent contributor to public discourse through lectures, reports, and media appearances, including the Reuters Institute Digital News Report. He is affiliated with research groups such as the Bergen Media Use Research Group and MediaFutures: Research Center for Responsible Media Technology & Innovation , where he contributes to interdisciplinary work on responsible media innovation.

Daniel Romeroمشاهده پروفایل

استاد

• UAV Communications
• Wireless Communications
• Time-Series Analysis
• +۵ مورد دیگر

**Daniel Romero** is a **Professor** in the **Department of Information and Communication Technology** at the **University of Agder**, Norway. His research focuses on UAV communications, time-series analysis using machine learning and network science, and decentralized processing for sensor networks. He holds a Ph.D. in Signal Theory and Communications from the University of Vigo (2015), an M.Sc. in Signal Theory (2011), and a Telecommunication Engineering degree (2009). **Education**: Ph.D. in Signal Theory and Communications, University of Vigo (2015) M.Sc. in Signal Theory and Communications, University of Vigo (2011) Telecommunication Engineering, University of Vigo (2009) **Research Interests**: His work spans UAV communication systems (focusing on low-latency, high-reliability networks), time-series analysis for complex systems (using ML and network science), and decentralized computation in sensor networks to improve robustness and hardware efficiency. Recent projects include radio map estimation for mmWave beam alignment, spoofing detection via graph neural networks, and aerial base station placement optimization. **Publications**: Over 30+ peer-reviewed articles in top venues like IEEE Transactions on Wireless Communications and ICC. Recent trends emphasize radio map estimation (2023–2024), UAV-enabled spectrum surveying (2022), and robust D2D communications (2022). **Advising & Grants**: Teaches PhD courses (Statistical Signal Processing, Advanced Optimization) and leads the **Advanced Signal Processing Lab (ASL)**. Collaborates with the **CIEM (Center for Integrated Emergency Management)** on crisis-related communication systems. **Labs/Teams**: Directs the Advanced Signal Processing Lab (ASL.uia.no) and contributes to CIEM, applying ML and signal processing to emergency management challenges.