جستجوی استاد راهنمای دانشگاهی

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

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

Rune Strandberg is an Associate Professor at the Department of Engineering Sciences, University of Agder. He holds a PhD in solar cell physics from NTNU and specializes in photovoltaic materials, solar cell physics, and energy conversion. His research focuses on advanced solar cell concepts including tandem cells, intermediate band solar cells, and thermoradiative energy harvesters. Education: Master of Technology (2005) and PhD (2010) in solar cell physics from NTNU. Pedagogical training includes Uniped courses (2014-2015) and PhD supervision qualification. Current teaching: Renewable Energy, Solar Energy Systems, Electromagnetism, Advanced Photovoltaics Prior roles: PhD student at NTNU (2005-2009), Senior Researcher at Teknova AS (2010-2013) Research areas: New photovoltaic concepts, characterization of photovoltaic cells, solar cell physics, emissive energy harvesters His recent publications analyze band gap optimization, radiative coupling in multi-junction cells, temperature sensitivity, and theoretical efficiency limits across multiple high-impact journals. Collaborators include Anne Gerd Imenes, Alfredo Sanchez Garcia, and Sissel Tind Kristensen. Key contributions include development of analytical models for solar cell performance, field testing of PV modules in Norway, and studies on temperature effects in multicrystalline silicon wafers.

• Solar Cell Physics
• Photovoltaic Materials
• Energy Conversion
• +۳ مورد دیگر

Charu Sharma is an Associate Professor in the Department of Electrical Engineering at UiT The Arctic University of Norway, specializing in power systems and smart grid technologies. Her work focuses on reactive power control, voltage stability, and optimization of renewable energy-integrated networks. Research on cyber-physical co-simulation frameworks for real-time grid management Development of hybrid renewable energy microgrids for rural and industrial applications Expertise in optimization algorithms (e.g., BFOA-PSO, ANFIS) for energy systems Recent publications highlight her contributions to DER-enriched distribution networks, low-inertia system stability, and intelligent load frequency control. She actively collaborates with researchers on projects like Cooperative Isolated Renewable Energy Systems and arcICE , addressing reliability and sustainability challenges.

• Power Systems
• Smart Grids
• Reactive Power Control
• +۳ مورد دیگر

Amir R. Nejad is a Professor in the Department of Marine Technology at NTNU, leading the Marine Energy Systems and Autonomics (MESA) research group. He holds roles such as Chair of the EAWE WindEurope Scientific Track Committee and co-chair of the Drivetrain Technical Committee at the European Academy of Wind Energy (EAWE). His research focuses on stochastic design, reliability-based operation, dynamic modeling, and fault detection in marine and offshore renewable systems. Nejad is an editorial board member for journals like Wind Energy Science and Ocean Engineering , and a member of ISO committees on drivetrain health monitoring. Education: PhD in Marine Technology, NTNU (2015) MSc Subsea Engineering, University of Aberdeen (2012) BSc Mechanical Engineering, Tehran University (2009) His research interests emphasize offshore wind drivetrain reliability, condition monitoring, and digital twin applications. Recent work includes studies on blade bearing fatigue estimation, SCADA-based lifetime extension of drivetrains, and wake steering techniques in floating wind farms. Key awards include multiple 'Best Lecturer' honors at NTNU and international conference recognitions. His research has been supported by grants such as the Nowitech Fellowship and DNV Education Fund. Key Projects: Marine System Dynamics and Vibration Lab (MD Lab) oversees projects on wind turbine drivetrains, floating offshore systems, and digital twin integration. Lab/Teams: MD Lab focuses on advancing marine energy systems through experimental and computational studies.

• Marine Renewable Energy
• Wind Energy Systems
• Drivetrain Design and Reliability
• +۷ مورد دیگر

Alvaro Fernandez Quilez is an Associate Professor in Artificial Intelligence at the Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, University of Stavanger. He leads the Stavanger AI Laboratory (SAIL), fostering interdisciplinary AI research with a focus on healthcare and education applications. Research Interests: His work centers on responsible AI, emphasizing ethics, fairness, transparency, and uncertainty in AI systems. He applies deep learning and machine learning techniques to medical imaging, particularly in prostate cancer and neurodegenerative diseases like Alzheimer’s and Parkinson’s. His research integrates algorithmic innovation with clinical relevance, addressing challenges in data scarcity, bias, and model interpretability. The recent publications highlight a strong trend in developing and evaluating AI models for diagnostic support in radiology and neurology. Key themes include uncertainty quantification, self-supervised learning, synthetic data generation via GANs, and fairness analysis across gender and centers. The work spans from foundational AI methods to their clinical translation in multi-center studies. Teaching and Academic Leadership: He coordinates the course DAT105 - AI for everyone and has contributed as a guest lecturer in bioinformatics, technological foundations, and PhD ethics, particularly on AI and ethics. He is also enrolled in a PhD supervisory qualification program, underscoring his growing role in graduate education. Advising and Grants: While specific students and grants are not listed in the text, his leadership of SAIL and active publication record suggest involvement in research supervision and project funding. His collaborations span multiple institutions and disciplines, indicating strong team-based research efforts. Laboratories and Teams: He leads the Stavanger AI Laboratory (SAIL), which serves as the central hub for AI research at the University of Stavanger, promoting collaboration across departments and with external partners in healthcare and technology.

• Artificial Intelligence
• Responsible AI
• AI Ethics
• +۱۳ مورد دیگر

Morten Hovd is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on advanced control systems, model predictive control (MPC), power electronics, and optimization algorithms. He has contributed significantly to the development of robust control strategies for uncertain systems and has published extensively in leading journals and conferences in the field of control engineering. His research interests span several key areas in control systems engineering, including model predictive control, nonlinear control systems, optimization under uncertainty, and applications in power systems and energy efficiency. He is particularly known for his work on discrete-time bilinear systems, modular multilevel converters (MMCs), and the integration of machine learning techniques with control theory. His contributions address both theoretical advancements and practical implementations in industries such as energy and petroleum engineering. Hovd's recent publications highlight advancements in energy-efficient control systems, stochastic surrogate modeling for subsurface flows, and optimization algorithms tailored for complex engineering problems. His work often combines rigorous mathematical frameworks with real-world applications, such as improving the reliability of power systems and enhancing reservoir management through data-driven methods. He is actively involved in teaching courses such as TTK4210 (Advanced Control of Industrial Processes) and TK8118 (Mini-seminar in Cybernetics). His research has led to innovations in fault detection for power systems, energy-efficient building climate control, and robust MPC strategies for uncertain systems.

• Control Systems
• Model Predictive Control
• Power Systems Engineering
• +۳ مورد دیگر

Per Gunnar Kjeldsberg is a Professor at the Department of Electronic Systems, Norwegian University of Science and Technology (NTNU), and currently serves as acting head of the institute. His research focuses on embedded heterogeneous multi-processor systems , particularly in multimedia and digital signal processing applications . He has led and participated in numerous national and international projects, including EU Horizon 2020 initiatives like READEX (as work package leader) and Tulipp (as principal researcher), and supervises the MSCA-IF project Palmera . Kjeldsberg is a Senior Member of IEEE and part of the European Network of Excellence HiPEAC . Education : Sivilingeniør (MSc) in Electrical Engineering (1992), PhD (2001) from Norwegian Institute of Technology (NTH)/NTNU His work spans energy-efficient computing , radiation-hardened memory design for space applications, and dynamic hardware management . Publications include co-authoring three books and over 150 peer-reviewed articles in journals and conferences. He leads the Circuit and Radio Systems group and drives a strategic NTNU initiative on Energy Efficient Computing Systems . Kjeldsberg has held visiting researcher roles at imec (Belgium), University of California, Irvine, imec Netherlands (Holst Centre), and University of New South Wales (Australia). Scientific Awards : Senior Member of IEEE Mikroelektronikkprisen (2006–2015)

• Embedded Systems
• Digital Signal Processing
• Multimedia Systems
• +۵ مورد دیگر

Pål Stabel Keim is an Associate Professor and Deputy Head of Education at the Department of Electric Energy , Norwegian University of Science and Technology (NTNU) , within the Faculty of Information Technology and Electrical Engineering . His work focuses on high voltage apparatus, electrical machines, and power electronics with a system-oriented perspective. University: NTNU School: Faculty of Information Technology and Electrical Engineering Department: Department of Electric Energy Academic Rank: Associate Professor Keim's research emphasizes insulation design for HVDC, HVAC, and inverter-fed machines, partial discharge analysis, and system-oriented design of electric machines. His work integrates digital tools to enhance electrical system design from generation to consumption. His recent publications explore modular HVDC wind drive trains, multi-agent control systems, aerospace power electronics, and dielectric insulation under combined voltages. These span disciplines including Electrical Engineering , Renewable Energy , and Aerospace Systems . 2015 : Nowitech Innovation award 2016 : Grønn fases energipris Keim has supervised Jaume Martí Cascalló (Master's thesis, 2020) and co-authored extensive research on high-voltage systems. He teaches courses in electrical machines, power systems, and energy infrastructure.

• High Voltage Apparatus
• Electric Ageing Mechanisms
• High Voltage Electric Machines
• +۱۲ مورد دیگر

Alexey Pavlov is a Professor of Petroleum Cybernetics at the Department of Geosciences and Petroleum, Norwegian University of Science and Technology (NTNU). He holds an MSc in Applied Mathematics from St. Petersburg State University, a PhD in Mechanical Engineering from Eindhoven University of Technology, and has industrial R&D experience from Statoil and Ford Motor Co. Education: MSc (Applied Mathematics, St. Petersburg State University), PhD (Mechanical Engineering, Eindhoven University) His research focuses on control systems for petroleum engineering applications, including nonlinear control theory, iterative learning control, and data-driven optimization methods. Publications reveal a strong emphasis on real-time drilling optimization, well integrity monitoring, and synchronization in networked systems. Recent work trends include machine learning integration for oil well monitoring, moment matching in model reduction, and extremum seeking control for multi-agent systems. Collaborations span institutions like Ford Motor Co., Statoil, and Eindhoven University of Technology. Current affiliations include the Department of Geosciences and Petroleum at NTNU. No scientific awards or advisee information is explicitly mentioned in the provided texts.

• Control Systems
• Petroleum Engineering
• Machine Learning
• +۳ 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.

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

Tor Skeie is a Professor at the University of Oslo's Department of Informatics, specializing in networks and distributed systems. His research focuses on high-performance networking, InfiniBand technologies, and adaptive routing systems. Current investigations include automated parameter tuning for reservoir simulations, adaptive routing in InfiniBand hardware, and modeling WiFi quality attenuation. His work develops efficient solutions for virtualized HPC environments and cloud computing infrastructures. Recent publications demonstrate innovations in network modeling, adaptive routing algorithms, and performance analysis of distributed systems. Research collaborations span European projects on high-performance networking infrastructures. Leads the Networks and Distributed Systems (ND) research group investigating fault-tolerant routing, network virtualization, and congestion control mechanisms.

• High-Performance Networking
• Distributed Systems
• InfiniBand Technology
• +۲ مورد دیگر

Idelfonso Nogueira is an Associate Professor at the Department of Chemical Process Technology, Norwegian University of Science and Technology (NTNU). His research focuses on integrating artificial intelligence, advanced control, and system optimization to address challenges in Industry 4.0/5.0. Key research areas include AI-driven product/process development, interpretable machine learning for process systems, multi-scale system integration, and adaptive digital twins. He collaborates extensively with global universities, institutes, and industries to advance sustainable industrial solutions. His work emphasizes digitalization, hybrid modeling, and optimization frameworks, as seen in publications on pressure swing adsorption, fragrance engineering, and CO2 capture systems. He also promotes innovative teaching methods, including augmented reality for chemical engineering education. Recent publications highlight advancements in digital twin frameworks, machine learning for crystallization processes, and optimal control strategies. His research bridges theoretical models with practical applications, ensuring robustness and reliability in industrial contexts.

• Applied Artificial Intelligence
• Digital Twins
• Hybrid Modeling
• +۴ مورد دیگر

Dr. Jacob Joseph Lamb is an Associate Professor at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Energy and Process Engineering within the Faculty of Engineering. He serves as the Study Program Manager for the Bachelor of Engineering in Renewable Energy (BIFOREN) and leads the Digital Energy Systems laboratory group. His roles include WP8 leadership in the FME Battery initiative and Theme Leader for Efficient Energy Use at ENERSENSE. Education: PhD in Energy and Process Engineering, NTNU (2013–2016) MSc in Chemical Engineering, University of Otago, New Zealand (2011–2012) BSc in Chemical Engineering, University of Otago, New Zealand (2008–2010) Research Interests: Dr. Lamb focuses on sustainable energy systems, particularly lithium-ion battery technologies , redox flow batteries , bioenergy , and digitization of energy systems . He explores sensor technologies for real-time monitoring and optimization of energy storage devices. His work integrates machine learning with physics-based models to enhance battery performance and reliability. Key Projects: FME Battery: Norwegian research initiative for advanced battery technologies ReZinc: Zinc-air flow battery development for stationary storage HeaLiSelf: Self-healing lithium-ion batteries using optical sensing and data analytics Computational studies on pre-lithiation processes and cold-climate battery performance Publications: His recent work emphasizes lithium-ion battery digitalization , SoC estimation , and electrode structuring . He bridges experimental and computational methods to address challenges in energy storage scalability and safety. Affiliations: ENERSENSE (Efficient Energy Use Theme) NTNU Thermal Engineering Laboratories

• Lithium-ion Battery Technologies
• Redox Flow Batteries
• Bioenergy
• +۵ مورد دیگر

Professor Adil Rasheed is affiliated with the Department of Engineering Cybernetics at the Faculty of Information Technology and Electrical Engineering , Norwegian University of Science and Technology (NTNU). His work focuses on integrating data-driven methods with physics-based modeling to create reliable hybrid systems for high-stakes applications. Research Interests : Bigdata Cybernetics, Hybrid Analytics / Modeling, Artificial Intelligence, Reduced Order Modeling, Computational Fluid Dynamics, Wind Energy, Autonomous Vessels, and Safe Reinforcement Learning. Digital Twin Applications : Professor Rasheed leads projects in Digital Twin technology for wind energy and smart greenhouses. His work includes autonomous marine navigation, federated learning for Industrial IoT, and predictive maintenance in offshore wind turbines using integrated data-driven models. Collaborative Efforts : He collaborates with industry partners on digital twin syncing for autonomous vessels, thermal zoning algorithms for building control, and anomaly detection in multivariate time series. His publications highlight the use of transformers, federated transfer learning, and corrective source terms in hybrid modeling.

• Bigdata Cybernetics
• Hybrid Analytics / Modeling
• Artificial Intelligence and Machine Learning
• +۱۰ مورد دیگر

Ole Andre Øiseth is a Professor at the Department of Structural Engineering, Norwegian University of Science and Technology (NTNU), specializing in structural dynamics with focus on bridges and marine structures. He leads the structural mechanics research group. Research Interests: Wind Engineering Bridge Aerodynamics Structural Health Monitoring Operational Modal Analysis Fluid-Structure Interaction Extreme Load Analysis Key Contributions: Developed nonlinear force models for bridge aerodynamics Advanced Kalman filter techniques for wind load identification Environmental contour methods for bridge design Automated monitoring systems for long-span bridges

• Structural Dynamics
• Bridge Engineering
• Wind Engineering
• +۶ مورد دیگر