جستجوی استاد راهنمای دانشگاهی

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

• Anesthesiology
• Emergency Medicine
• Cardiopulmonary Resuscitation (CPR)
• +۳ مورد دیگر

Eirik Skogvoll is a Professor at NTNU (Norwegian University of Science and Technology) and a pediatric anesthesiologist at St. Olav's University Hospital. He holds a leadership role as Vice Head for Research in the ISB Research group within the Faculty of Medicine and Health Sciences. His primary affiliation is with the Department of Circulation and Medical Imaging. Skogvoll's academic background includes an MD (1985), PhD (2000), and certifications in anesthesiology (1993) and pediatrics (2005). He has been actively involved in clinical work, teaching, and research at NTNU since 1994. Notably, he served as Head of the Resuscitation Committee at St. Olav's Hospital from 2008 to 2019 and was a visiting professor at the University of Pennsylvania in 2017. His research focuses on anesthesiology, emergency medicine, and cardiopulmonary resuscitation (CPR), with a particular emphasis on clinical circulatory physiology and medical statistics. He leads the Norwegian Cardiac Arrest Registry and has contributed to courses like Pediatric Emergencies and Medical Statistics in Clinical Analysis. Recent articles highlight advancements in AI-driven diagnostics (e.g., deep learning for ECG analysis), optimization of CPR techniques, and pediatric cardiac arrest epidemiology. His work bridges clinical practice and technological innovation, aiming to improve patient outcomes in critical care scenarios.

Anne Kjersti C. Befringمشاهده پروفایل

استاد

• Health Law
• Administrative Law
• Constitutional Law
• +۱۲ مورد دیگر

Anne Kjersti C. Befring is a Professor at the University of Oslo 's Department of Public Law. She is one of the Nordic countries' leading experts in health law and life sciences , with a focus on genetic mapping , AI in healthcare , and data privacy regulations . Doctorate in Law (2019) on Legal Perspectives on Personalized Medicine Co-founder of the Nordic PerMed Law network (2019-2021) Current legal researcher in the Morgi life science project (2022-present) Her research spans health law , administrative law , and constitutional law , particularly examining how GDPR intersects with national healthcare regulations, patient rights in data sharing, and ethical issues in gene therapy. Recent publications focus on: AI's impact on medical decision-making Legal frameworks for biobanks Personalized medicine governance Pandemic emergency legal measures Laboratory data privacy protocols She supervises PhD candidates Vibeke Binz Vallevik (synthetic data for AI) and Ole Boye (gene therapy). Befring served as Deputy Chair of Norway's National Research Ethics Committee for Medicine and Health Sciences (NEM) from 2022-2025 and has contributed to critical policy discussions around data ethics and public health governance .

• Applied Artificial Intelligence
• Digital Twins
• Hybrid Modeling
• +۴ 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.

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

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

Martin Wulf Gerdesمشاهده پروفایل

دانشیار

• eHealth
• Artificial Intelligence
• Machine Learning
• +۹ مورد دیگر

Martin Wulf Gerdes is an Associate Professor at the Department of Information and Communication Technology, University of Agder, Norway. With a Ph.D. in eHealth and over two decades of experience in telecommunications and healthcare technology research, he focuses on developing user-friendly, interoperable eHealth solutions using ICT and IoT. His career spans roles at Ericsson Eurolab and academic leadership in Norway. Education: M.Sc. equivalent in General Electrical Engineering (1998) from RWTH Aachen, emphasizing ICT. Ph.D.: Information and Communication Technologies with eHealth specialization (2019) from University of Agder. Research Interests: Gerdes explores AI-driven decision support systems, distributed eHealth infrastructure, telemedicine, and assistive technologies for chronic disease management. His work bridges IoT, data privacy, and user-centered design in healthcare robotics. Teaching: Since 2014, he has taught at bachelor's and master's levels, currently leading courses in Communication & Cooperation, Technology Understanding, and seminar projects in eHealth. Scientific Publications: His 15 most recent articles (2020-2024) span AI in dental radiology, blood glucose prediction, digital twins, and ethical considerations in healthcare algorithms. Topics include IoT integration, data privacy, and pandemic analytics.

• Artificial Intelligence
• Process Control
• Systems Optimization
• +۴ مورد دیگر

Idelfonso Nogueira is an Associate Professor in the Department of Chemical Engineering at NTNU. His research focuses on integrating artificial intelligence, advanced control systems, and digitalization to address industrial challenges, particularly in chemical processes and product development. He leads the AiP²S² framework, emphasizing synergies between AI and traditional engineering methods. Education: PhD in Chemical and Biological Engineering (University of Porto, 2018), Visiting Researcher at Tampere University of Technology (2016–2018), and multiple master’s degrees from Brazilian and Portuguese institutions. Research Interests: Process optimization using AI, hybrid modeling, digital twins, and Industry 4.0/5.0 applications. Collaborations span universities, institutes, and industries globally. Teaching focuses on preparing engineers for modern industrial demands. Publications: Over 60 manuscripts, with 70% in Q1 journals (e.g., Chemical Engineering Science , Computers and Chemical Engineering ). Key areas include pressure swing adsorption, machine learning for process control, and fragrance molecule design. Grants/Advising: Active in parameter estimation, process modeling, and renewable energy projects. No explicit student advising list provided. Labs/Teams: Engages in AI-driven process systems engineering, digital twin development, and sustainable technology initiatives.

• Computational Physiology
• Biomechanics
• Cardiac Electromechanics
• +۷ مورد دیگر

Joakim Sundnes is a Chief Research Scientist and Research Professor at the Department of Scientific Computing, Simula Research Laboratory. He specializes in computational physiology, cardiac biomechanics, and mathematical modeling of cardiovascular systems. Key Research Areas: Cardiac electromechanics, computational fluid dynamics in cardiology, uncertainty quantification in cardiac models, and mechano-electric feedback mechanisms Recent Trends: Focus on patient-specific modeling, left atrial flow dynamics, right ventricular mechanics in pulmonary hypertension, and personalized treatment simulations Scientific Contributions: Active participant in international conferences and editorial work. Co-author of multiple benchmark studies and educational texts on physiological modeling.

• Artificial Intelligence
• Machine Learning
• Case-Based Reasoning
• +۷ مورد دیگر

Professor Kerstin Bach is affiliated with the Department of Computer Technology and Informatics at NTNU's Faculty of Information Technology and Electrical Engineering. Her research focuses on artificial intelligence, machine learning, and their applications in health informatics and robotics. Notable projects include the selfBACK app for musculoskeletal pain management and reinforcement learning for robotic systems. She has supervised numerous doctoral students in areas like activity recognition and explainable AI. Education: Formal academic degrees not explicitly listed in text. Research interests span: - Case-Based Reasoning for clinical decision support - EHealth/mHealth applications (e.g., wearable sensor analytics) - Explainable AI methodologies - Human activity recognition using accelerometer data Recent work emphasizes: - Machine learning models for health monitoring (sleep/wake detection, gait analysis) - Reinforcement learning for robotics and autonomous systems - AI-driven clinical tools for pain management Key Projects: selfBACK app: Digital self-management tool for musculoskeletal pain (RCT validated) UtiliGEM: Energy management framework for IoT devices Labs/Teams: Active in NTNU's AI research groups focusing on healthcare informatics and robotics. Collaborates with medical institutions on clinical AI applications.

• Underground Hydrogen Storage
• CO2 Sequestration
• Geological Porous Media
• +۴ مورد دیگر

Raoof Gholami is a Professor of Energy Resources at the University of Stavanger's Faculty of Science and Technology, Department of Energy Resources. His research focuses on subsurface energy storage, particularly hydrogen and CO₂ sequestration in geological formations. He has contributed extensively to understanding mechanisms like hydrogen leakage in salt caverns, CO₂-mineral interactions in chalk reservoirs, and bio-reactive transport modeling in storage sites. Key research interests include: (1) Hydrogen storage in porous media, (2) CO₂ sequestration risks and geomechanical stability, (3) Reactive transport modeling, and (4) Machine learning applications in reservoir characterization. His work bridges experimental studies (e.g., salt precipitation dynamics) with numerical modeling (e.g., caprock integrity analysis). Recent publications emphasize hydrogen storage challenges (2025), CO₂ storage in chalk (2023), and leakage risk assessment frameworks (2021). His research also explores novel materials like biodegradable surfactants for drilling fluids (2023) and advanced well path design techniques (2022). Collaborations span institutions globally, addressing topics like shale reservoirs, geothermal energy, and subsurface imaging.

• Digitalization
• Control Systems
• Optimization
• +۴ مورد دیگر

Guoyuan Li is a Professor at the Department of Marine Operations and Engineering Technology at NTNU, affiliated with the Faculty of Engineering. His research focuses on digitalization, control systems, robotics, maritime operations, and human-machine interaction. He holds IEEE Senior Membership (2019) and editorial roles in IEEE Journal of Oceanic Engineering and IEEE Transactions on Intelligent Transportation Systems . Education includes a Ph.D. in Computer Science from Hamburg University (2013), M.S. and B.S. from Chongqing University (2009/2006). Key projects include EU-funded RoboSAPIENS (robotic adaptation), Digital Twin for Green Ship Operations, and TwinShip (vessel lifecycle services). Recent awards include 2024 IEEE Robotics & Automation Magazine Best Paper and multiple IEEE conference recognitions. His work spans AI-driven maritime safety, digital twin applications, and autonomous systems. Visit Intelligent Systems Lab for more.

• Energy Efficiency
• Energy Systems
• Renewable Energy
• +۳ مورد دیگر

Natasa Nord is a Professor at the Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU). Her research focuses on energy efficiency, energy supply systems, district heating, and zero-energy building technologies. She leads projects like the CETPartnership, which explores thermal storage in energy systems, and the EU-funded ARV project, aiming to create climate-positive circular communities through building renovation and energy system integration. Her work emphasizes sustainable energy transitions, including the integration of renewable energy sources, thermal energy storage, and data-driven approaches for optimizing district heating systems. She collaborates on projects such as analyzing waste heat utilization from data centers, optimizing heating systems for cruise ships, and modeling energy pathways for university campuses. Her research spans topics like CO₂ heat pump performance, phase change material (PCM) storage, and smart grid integration. Education: Not explicitly stated in the provided text. Research Projects: CETPartnership: Thermal storage potentials in energy systems. ARV (H2020): Building renovation and climate-positive communities. Key Contributions: Advancing district heating optimization. Developing predictive models for energy systems. Exploring CO₂ heat pump and PCM storage applications. Her advising and grants involve guiding students on topics like thermal storage, energy efficiency in buildings, and data center waste heat recovery. She collaborates with institutions like SINTEF and leads interdisciplinary teams for energy system modeling and sustainability planning.

• Dynamics of long-span suspension bridges
• Floating bridges
• Stochastic environmental wind and wave loading
• +۷ مورد دیگر

Øyvind Wiig Petersen is an Associate Professor at the Department of Structural Engineering, Norwegian University of Science and Technology (NTNU). His research focuses on bridge dynamics, wind and wave loading, inverse force identification, structural monitoring, and machine learning applications in structural mechanics. He works extensively with long-span suspension bridges and floating bridge systems. Current research areas include vortex-induced vibrations, Kalman filter applications, wind tunnel testing, and finite element model updating. He has published in leading journals like Journal of Wind Engineering, Mechanical Systems and Signal Processing, and Engineering Structures. His work integrates experimental data with computational models for structural condition assessment and load estimation.

• Environmental technology
• Chemical process technology
• Mathematical modeling and numerical methods
• +۹ مورد دیگر

Tiina Komulainen is a Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, specifically within the Department of Mechanical, Electronics and Chemistry. Her work focuses on environmental technology, biogas production, and wastewater treatment through advanced simulation and control engineering techniques. Key affiliations: Applied Artificial Intelligence research group Collaborative projects: MaxBiogas (with OsloMet and Veas) Research themes: Digital competence development in water industry, smart water technology Her research integrates mathematical modeling, machine learning, and technical cybernetics to optimize biological wastewater processes. Recent projects involve virtual sensors, nutrient estimation, and dynamic simulation for sustainable biogas production. Publications highlight collaborations with international institutions like Linköping University and Mälardalen University, focusing on municipal MBBR processes, energy-efficient wastewater treatment, and district heating network optimization. She contributes to educational advancements through simulation-based learning technologies and has developed teaching materials on model predictive control at Metropolia University of Applied Sciences.