جست‌وجوی استاد و پژوهشگر

Mostafa Mohammadiمشاهده پروفایل

استادیار

• Neurorehabilitation Robotics
• Human-Computer Interaction
• Telerehabilitation Technologies
• +۵ مورد دیگر

Mostafa Mohammadi is an Assistant Professor at the Department of Health Science and Technology, Aalborg University, affiliated with the Faculty of Medicine and Center for Rehabilitation Robotics. His research focuses on neurorehabilitation robotics, human-computer interaction, and telerehabilitation technologies for individuals with disabilities. Ph.D. in Biomedical Engineering (Aalborg University, 2018-2022) M.Sc. in Biomedical Engineering (Polytechnic University of Milan, 2015-2017) B.Sc. in Mechanical Engineering (Sharif University of Technology, 2011-2015) His work spans exoskeleton design, assistive robotics, and innovative interfaces like tongue-computer interfacing. Recent projects include the eMotivo digital health solution funded by Innovation Fund Denmark and intelligent tendon-driven exoskeletons for severe disabilities. Research outputs (33 total) emphasize adaptive robotics, motor impairment solutions, and wearable technologies. Collaborations span robotics, biomedical engineering, and clinical disciplines. Teaching includes courses in rehabilitation robotics, human-computer interaction, and digital systems for biomedical engineering. Scientific activities align with UN Sustainable Development Goals for quality education and reduced inequalities. Notable contributions include advancements in myoelectric interfaces and neurorehabilitation technologies.

Per Lynggaardمشاهده پروفایل

استاد

• Integrated Circuit Design
• Wireless Sensor Networks (WSN)
• Machine Learning
• +۸ مورد دیگر

Per Lynggaard is a Professor of Electronics at the Technical University of Denmark (DTU) , leading the B.Eng. program in Electronics. Previously, he held an Associate Professor role at Aalborg University, combining academic excellence with a robust industrial career in technical-scientific research and development. Education: M.Sc. in Electrical Engineering and Information Technology (EE and IT) Ph.D. in Electronics from Aalborg University Research Interests: Focus on Integrated Circuit Design, Wireless Sensor Networks (WSN), Machine Learning, IoT, and Smart City Technologies . His work emphasizes energy-efficient systems, cybersecurity in IoT, AI-driven interference mitigation, and sustainable energy harvesting solutions. He has contributed to UN Sustainable Development Goals through projects addressing smart infrastructure and environmental monitoring. Projects & Collaborations: Leads and participates in EU-funded initiatives such as InnoTech (2023–2025) for green transition solutions and TransportTech (2023–2026) for Industry 4.0 logistics. Active in cybersecurity research via projects like Jamming Against Critical Wireless Communication , aiming to protect critical infrastructure. Awards: Recognized with multiple honors and rewards during his industrial career, though specific names are not listed. His work has been cited widely, with notable impact in IoT security and energy-efficient systems. Advising & Grants: Supervises Turnip T.N. in a PhD project on 6G security protocols. Engaged in securing funding for projects like F2D2: The Community for Dynamic Data (2021–2030), focusing on dynamic data systems and cybersecurity. Labs & Teams: Collaborates in interdisciplinary teams such as the InnoTech TaskForce and F2D2 Community , advancing IoT and AI integration. His research bridges academia and industry, with outputs spanning smart cities, healthcare IoT, and sustainable energy systems.

Yan Kyaw Tunمشاهده پروفایل

استادیار

• Edge Computing
• Multi-Access Edge Computing
• Resource Allocation
• +۱۲ مورد دیگر

Yan Kyaw Tun is a Tenure Track Assistant Professor in the Department of Electronic Systems at Aalborg University's Technical Faculty of IT and Design, located in Copenhagen, Denmark. His research lies at the intersection of wireless communications, edge computing, and artificial intelligence, with a strong focus on next-generation networks (5G/6G), UAV-assisted systems, and intelligent resource management. His educational background includes a Ph.D. in Computer Engineering from Kyung Hee University, South Korea, where he was awarded the Best Ph.D. Thesis Award in 2021, and a Bachelor of Engineering in Marine Electrical Systems and Electronic Engineering from Myanmar Maritime University. Dr. Tun's research interests span Edge Computing , Multi-Access Edge Computing (MEC) , Resource Allocation , Unmanned Aerial Vehicles (UAVs) , Reinforcement Learning , Energy Efficiency , and Integrated Sensing and Communication (ISAC) . His work leverages AI and optimization techniques to enhance the performance of wireless networks, particularly in space-air-ground integrated systems and satellite-HAP environments. The recent publications highlight a clear trend toward intelligent and sustainable networking: the integration of STAR-RIS (Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces), Federated Learning for satellite-HAP systems, and AI-driven optimization for UAV trajectories and beamforming. These works are published in high-impact venues such as IEEE Transactions on Mobile Computing and IEEE ICC , showcasing his leadership in cutting-edge communication technologies. His scientific accolades include: IEEE ComSoc Outstanding Young Researcher Award for EMEA Region (2024) Best Ph.D. Thesis Award (2021) Student Best Paper Award at APNOMS 2019 Korea Network Operation and Management Conference Award (2020) Korea Computer Congress 2018 Award Dr. Tun is actively engaged in the academic community as an advisor and grant participant. Though no direct advisees are listed, his involvement in large collaborative projects—evidenced by co-authorship with senior researchers like Prof. Choong Seon Hong—indicates mentorship and team leadership. He has served on the editorial boards of IEEE Internet of Things Journal , IEEE Open Journal of the Communications Society , and IEEE Network , and has secured research support through participation in IEEE-organized workshops and special issues. He is a key organizer of upcoming workshops, including the 'Sustainable AI for Next-Generation Wireless Communications and Networking' at IEEE GLOBECOM 2025 and the 'Digital Twin Networks' workshop at IEEE/CIC International Communications in China 2025, reflecting his role in shaping future research directions in intelligent and green networking.

Naeem Ayoubمشاهده پروفایل

استادیار

• Machine Learning
• Computer Vision
• Robotics
• +۱۲ مورد دیگر

Naeem Ayoub is an Assistant Professor in the Department of Technology and Innovation at the University of Southern Denmark (SDU), affiliated with SDU Technology Entrepreneurship and Innovation. His research bridges computer science and engineering, focusing on intelligent systems and automation. Research Interests: His work spans machine learning, computer vision, robotics, and cyber-physical systems. He explores applications in autonomous drones, digital twins, power line inspection, and environmental monitoring. His research emphasizes real-time decision-making, energy efficiency, and anomaly detection in complex systems. The recent publications highlight a strong trend in deploying AI-driven robotics for industrial and environmental applications, particularly in infrastructure inspection and predictive maintenance. His work integrates neural networks, sensor networks, and autonomous navigation to solve practical engineering challenges. Scientific Contributions: Active contributor to 19 research outputs including journals and conference proceedings. Creator of an open-source dataset for pylon component and fault detection using machine learning. Involved in interdisciplinary collaborations across engineering, environmental science, and computer science. Advising and Grants: While no formal students are listed, he has participated in academic supervision as a censor in internal examinations. His projects suggest involvement in research grants related to autonomous systems and industrial digitalization, though specific funding details are not provided. Labs and Teams: He collaborates within research teams focused on robotics and intelligent systems at SDU, particularly in drone technology and cyber-physical systems. His work involves close collaboration with researchers in environmental monitoring, power systems, and industrial automation.

Asmus Skar Christiansenمشاهده پروفایل

دانشیار

• Pavement Engineering
• Geotechnical Engineering
• Sensing Technologies
• +۴ مورد دیگر

Asmus Skar Christiansen is an Associate Professor in Pavement Engineering at the Department of Environmental and Resource Engineering, Technical University of Denmark (DTU Sustain). He serves as Head of Study for the Nordic Master in Cold Climate Engineering programme and lectures on pavement engineering, Arctic road construction, and foundation design. His academic career at DTU spans from Postdoc researcher (2017-2019) to Assistant Professor (2020-2023) and current Associate Professor position since 2023. His research centers on pavement technology and geotechnics with specialization in: Development of advanced testing and modeling techniques for pavements Integration of modern sensing technologies in civil infrastructure Computational mechanics for soil-structure interaction Sustainable materials for cold climate engineering Recent work demonstrates a clear shift toward IoT-enabled monitoring systems and data-driven pavement assessment, with 80% of 2023-2025 publications focusing on sensor integration and machine learning applications. Notable scientific contributions include: Creation of open-source datasets (LiRA-CD, RIVA) for road condition modeling Development of thermomechanical models for heated pavements Innovations in waste soil reuse for infrastructure He actively supervises PhD candidates across multiple projects including GREENPIPE (self-sensing pipe systems) and urban pavement analysis, while maintaining industry consultancy through COWI A/S collaborations. Christiansen also contributes to sustainable infrastructure through DTU's alignment with UN SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities).

Filipe Rodriguesمشاهده پروفایل

دانشیار

• Machine Learning
• Intelligent Transportation Systems
• Transportation Science
• +۷ مورد دیگر

Filipe Rodrigues is an Associate Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), where he conducts research in intelligent transportation systems and transportation science. His work integrates machine learning, artificial intelligence, and behavioral modeling to improve urban mobility and public transport systems. His research interests lie at the intersection of machine learning , transportation science , and behavioral modeling . He specializes in discrete choice modeling , reinforcement learning , graph neural networks , and smart card data analytics . His work contributes to sustainable urban mobility, leveraging big data and AI for proactive traffic control and public transport optimization. The recent publications highlight a strong trend toward integrating AI and behavioral science in transportation. Key themes include ride-sourcing driver behavior , public transport trip validation , autonomous fleet control , and causal machine learning . These works predominantly employ deep learning , Bayesian modeling , and offline reinforcement learning techniques, often applied to real-world datasets from Denmark and beyond. Scientific Contributions: Active contributor to journals like Transportation Research Part C and Journal of Choice Modelling . Supervises multiple PhD projects on AI in transportation and causal modeling. Regular presenter at major transportation and AI conferences. Advising and Grants: Filipe Rodrigues is the main or co-supervisor of several PhD students including O. B. Lassen, F. M. F. Santos, A. Nguyen, and X. Wu. He leads and participates in funded research projects such as 'Proactive traffic control through AI and Big Data' and 'Causal Graph Neural Networks for machine learning meta-modelling', indicating sustained grant support. His collaborative network spans institutions in Europe and beyond. Labs and Teams: He is part of the Intelligent Transportation Systems research group at DTU, collaborating closely with researchers like F. C. Pereira and C. M. L. Azevedo. The team focuses on data-driven mobility solutions, combining simulation, machine learning, and behavioral insights.

• Quantum Physics
• Quantum Communication
• Quantum Sensing
• +۴ مورد دیگر

Eugene Simon Polzik is a Professor of Physics at the Niels Bohr Institute, University of Copenhagen , and the founder of the Quantum Optics Center (QUANTOP) . He currently leads the Copenhagen Center for Biomedical Quantum Sensing and has held significant roles including Head of the Quantum Optics and Atomic Physics Division (2012-2021). PhD and MSc from Leningrad University Research Interests: polzik specializes in quantum physics, focusing on quantum communication , quantum sensing , and quantum information technologies . His groundbreaking work includes: Quantum teleportation between material objects Quantum memory for light Optical radio wave detection using nanomechanical oscillators Measurements beyond Heisenberg uncertainty limits Recent Publications span quantum sensing, optomechanics, and biomedical applications. Key articles include hybrid quantum networks, squeezed light generation, and advanced magnetometry techniques. Awards: Herbert Walther Award (2020) ERC Advanced Grants (2011, 2018) Villum Investigator (2019) Knight of Dannebrog (2018) Scientific American Research Leadership (2007) Grants: polzik has secured major funding including 150 MDKK Novo Nordisk Center for Biomedical Quantum Sensing (2024-2030) and 125 MDKK for QUANTOP.

Zhanhao Zhangمشاهده پروفایل

پژوهشگر ارشد

• Model Predictive Control
• Process Control
• Time-Delay Systems
• +۳ مورد دیگر

Zhanhao Zhang is a Postdoc researcher at the Department of Applied Mathematics and Computer Science (DTU Compute) of the Technical University of Denmark, affiliated with the Scientific Computing Center for Energy Resources Engineering. His work centers on developing advanced control strategies for industrial processes with emphasis on sustainability and computational efficiency in energy-intensive sectors. His research specializes in Model Predictive Control (MPC) for time-delay systems, numerical discretization methods, and real-time implementation of cyber-physical control systems. Key application domains include zero-emission industrial processes, cement production blending operations, and oil/gas zero-discharge systems, where he bridges theoretical control algorithms with practical industrial constraints through close industry collaboration. Analysis of his 2022-2024 publications reveals consistent innovation in extending MPC methodologies to handle complex industrial realities like time delays and discontinuities while targeting environmental sustainability. His work demonstrates strong integration of computational mathematics with process engineering to solve critical challenges in carbon-intensive industries. Zhang completed his PhD project "Model Predictive Control for Zero Emission Industrial Processes" (2021-2024) under Professor John Bagterp Jørgensen at DTU, resulting in his doctoral thesis and multiple high-impact publications. This industry-collaborative project focused on developing control frameworks for decarbonizing industrial operations. As an active member of DTU's Scientific Computing Center for Energy Resources Engineering, he contributes to interdisciplinary research that applies high-performance computing and mathematical modeling to optimize energy resource extraction, processing, and utilization while reducing environmental impact.

• Image Registration
• Image Segmentation
• Image Classification
• +۶ مورد دیگر

Sune Darkner is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in the Image Analysis, Computational Modelling, and Geometry research section. His work focuses on medical image processing with particular emphasis on neuro-imaging data including MRI and PET scans. His primary research interests include Image Registration, Segmentation and Classification of Medical Image Data , with a specific focus on estimation of image similarity as his main research interest. Darkner strongly believes that the implementation of image processing algorithms should be thoroughly tested and reflect the theoretical properties as accurately as possible. His work primarily centers on neuro-imaging data such as MRI and PET. His recent publications (2024-2025) reveal a strong focus on medical image analysis, with particular emphasis on tumor volume delineation, deformable image registration with physics constraints, and applications of deep learning in medical imaging. His work spans both theoretical foundations of image processing and practical clinical applications. Darkner previously held a Post Doc position at the Technical University of Denmark from February 2009 to January 2010, demonstrating his longstanding engagement with image analysis research in the Danish academic community.

Ole Madsenمشاهده پروفایل

استاد

• Robotics
• Automation
• Industry 4.0
• +۶ مورد دیگر

Ole Madsen is a Professor at the Department of Materials and Production within The Faculty of Engineering and Science at Aalborg University . His research focuses on Robotics and Automation , particularly in 5G Smart Production , AI for Manufacturing , and Industry 4.0 applications. He also holds a part-time position at Adding Robotics , applying his expertise in robot integration for industrial and healthcare settings. Research Interests : Robotics, Automation, AI, Sensor Systems, Modular Manufacturing, Welding Technology, Digital Twins, Human-Centered Robotics, Industry 4.0 His work spans 35+ years with over 205 publications , emphasizing smart production systems and robot-assisted processes . Key contributions include Swarm Production Architectures , 5G-Enabled Robotics , and Human-Robot Collaboration frameworks. He has supervised 10 PhD students and led major projects like RAU (Robot-Assisted Ultrasound) and GINP: Robotics & AI Innovation Network . Scientific Awards : SCAP2020 Best Presentation Award (2020) Ole's 31 projects include AP2030: Aseptic Factory 2030 (pharma production), AddSmart (robotics R&D), and 5G-Enabled Autonomous Systems . His 127 press/media mentions highlight advancements in robotic welding , industrial metaverse , and swarm production . He maintains an ORCID: 0000-0003-2133-2541 with extensive publication records across Swarm Robotics , Modular Manufacturing , and AI-Driven Production .

Shuai Zhaoمشاهده پروفایل

استادیار

• Power Electronics
• Reliability Engineering
• Machine Learning
• +۴ مورد دیگر

Shuai Zhao is an Assistant Professor at the AAU Energy Department, Faculty of Engineering and Science, Aalborg University. His research focuses on applying machine learning and artificial intelligence techniques to enhance reliability and condition monitoring in power electronic systems, with specific interests in lifetime estimation, fault diagnosis, and health management of critical components like capacitors and semiconductor devices. Institution: Aalborg University School: Faculty of Engineering and Science Department: AAU Energy Email: szh@energy.aau.dk His research spans multiple domains including: Physics-informed machine learning for power converter systems Remaining useful life prediction with hybrid Bayesian deep learning Thermal transient analysis and stress emulation methods IoT-enabled monitoring schemes for semiconductor devices Neural network applications in lithium-ion battery prognostics Recent publications show a strong trend toward integrating domain-specific physics with machine learning frameworks to address real-world challenges in: Power electronics reliability under operational stress Anomaly detection in multivariate time-series data Robust fault diagnosis for railway traction systems Temperature estimation in electric vehicle motors Imbalanced data handling in diagnostic systems Capacitance degradation modeling under environmental factors Current projects demonstrate collaboration with leading institutions on: AI-assisted long-term maintenance strategies Physics-informed neural network architectures Smart agricultural monitoring systems via IoT platforms Advanced particle filter methods for life prediction

Horst-Gunter Rubahnمشاهده پروفایل

استاد

• Nanofiber Materials Science
• Organic Solar Cells
• Nanostructure Materials
• +۷ مورد دیگر

Horst-Gunter Rubahn serves as Head of Department and Professor at the Mads Clausen Institute (MCI) within the University of Southern Denmark, and also leads the SDU Climate Cluster. With an extensive publication record of 480 publications, his academic career demonstrates significant leadership and research impact in materials science and nanotechnology. Dr. Rubahn's research focuses on construction of organic nanostructures , with particular expertise in nanofibers, organic solar cells, nanostructures, surface plasmonics, and thin films. His work spans multiple disciplines including materials science, environmental monitoring, energy storage, and sensor technology. The fingerprint analysis of his research shows strong connections across 12 similar research profiles with significant contributions to nanofiber materials science (100%), organic solar cells (60%), nanostructure materials (48%), and surface plasmonics (48%). His recent publications demonstrate trends toward practical applications of nanotechnology in environmental monitoring (nanoplastic detection), energy storage (eutectic salt hydrate composites), and food safety (electronic nose technology for poultry freshness). These works show increasing interdisciplinary collaboration across materials science, environmental science, and engineering disciplines. Villum Grant - Villum Experiment (2022) for research on perovskite solar cells and metal oxide interfaces Dr. Rubahn has supervised PhD students (2 listed in records) and leads multiple significant research projects totaling six active and completed initiatives. His current projects include NANOCHEM (Ultrahigh resolution chemical characterisation, 2022-2028), Motorvej For Materialeviden (2024-2027), and Perovskite solar cell stabilization research (2023-2025). His work has attracted substantial funding from both public and private sources, with research grants spanning energy storage, environmental monitoring, and advanced materials development. He leads research teams working on nanoscale materials characterization, with particular focus on the Mads Clausen Institute's facilities. His research groups collaborate extensively across international boundaries, with visible network connections across multiple countries as shown in his collaboration map. The teams focus on developing advanced materials for energy applications, environmental monitoring solutions, and novel sensing technologies with practical industrial applications.

Mateja Novakمشاهده پروفایل

استادیار

• Model Predictive Control
• Power Electronics
• Multilevel Converters
• +۶ مورد دیگر

Mateja Novak is an Assistant Professor at AAU Energy, Aalborg University, Denmark, within the Department of Applied Power Electronic Systems under the Faculty of Engineering and Science. Her research focuses on model predictive control, multilevel converters, machine learning, and reliability of power electronic systems, contributing to sustainable energy systems and renewable energy integration. She holds a Ph.D. from Aalborg University (2020) and an M.Sc. from Zagreb University (2014). Previously, she was a Postdoc at AAU Energy (2020-2023) and a visiting researcher at Kiel University (2018) and Danfoss (2023). Notable achievements include the EPE Outstanding Young EPE Member Award (2019) and 2nd place in the 2021 IEEE-IES Student and YP Competition. Her work spans projects like ALL2GaN (2023-2026) and AI-Power (2022-2027), addressing GaN IC solutions and AI-driven power electronics advancements. She is actively involved with IEEE societies including the Power Electronics Society and IEEE Women in Engineering. Her research outputs emphasize control strategies for power electronics, reliability analysis, and optimization techniques. Key areas of exploration include thermal stress balancing in converters, statistical model checking, and multiobjective control algorithms. Collaborations with industry partners like Danfoss and academic institutions like Kiel University underscore her interdisciplinary approach to advancing power electronics technology.

Henning Tangen Søgaardمشاهده پروفایل

دانشیار

• Mechanical Engineering
• Agricultural Engineering
• Control Systems
• +۴ مورد دیگر

Henning Tangen Søgaard is an Associate Professor at the Department of Mechanical and Production Engineering, part of AU Engineering at Aarhus University. He teaches mathematics, numerical methods, and mathematical statistics to BEng students. His research focuses on robotics in agriculture , dynamic modeling , and precision agriculture . Primary Affiliation: Department of Mechanical and Production Engineering, AU Engineering, Aarhus University Research Expertise: Computer vision, control systems for agricultural robotics, environmental modeling (ammonia emissions, spray drift), and wireless sensor networks. His work includes developing autonomous systems for weed control, GPS-based geo-referencing of crops, and mathematical models for fertilizer-related emissions. Publications span peer-reviewed journals and reports in agricultural engineering , robotics , and environmental science . Scientific awards are not mentioned in the provided text. He has no listed PhD students but has collaborated on multiple projects. For direct contact, his email is hts@mpe.au.dk .

Adem R. N. Aouichaouiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Learning
• Molecular Property Prediction
• +۶ مورد دیگر

Adem R. N. Aouichaoui is a postdoctoral researcher at the Department of Energy Conversion and Storage (Technical University of Denmark). His work focuses on machine learning applications in chemical engineering, particularly for molecular property prediction and hybrid modeling systems. Research areas: Machine Learning, Deep Learning, Graph Neural Networks Specialization: Data-driven process optimization and digital transition in (bio)chemical engineering Recent work includes real-time optimization in industrial processes (Viking Malt A/S case study) and self-consistent graph neural networks for thermodynamic property prediction. He actively contributes to AI applications in energy systems and process engineering. Contact: arnaou@kt.dtu.dk | arnaou@dtu.dk