جست‌وجوی استاد و پژوهشگر

Jakob Juul Larsenمشاهده پروفایل

دانشیار

• Signal Processing
• Machine Learning
• Geophysics
• +۳ مورد دیگر

Jakob Juul Larsen is an Associate Professor in the Department of Electrical and Computer Engineering at Aarhus University, specializing in Signal Processing and Machine Learning for geophysical applications. His research focuses on developing advanced instrumentation and data processing techniques for groundwater resource characterization. His core research domains include: Signal Processing for geophysical instrumentation Machine Learning applications in geophysics Hydrogeophysics and groundwater mapping Surface Nuclear Magnetic Resonance (SNMR) methods Transient Electromagnetic (TEM) systems Deep learning for electromagnetic data interpretation Dr. Larsen leads multiple significant research initiatives: SuperTEM (2021-2024): Monitoring groundwater resources for sustainable exploitation Surface NMR with long excitation pulses (2021-2022) Flood and Drought – Tracking water in shallow subsurface (2019-2023) GIRem – Guided Injection Remediation (2018-2022) MapField – Field-scale nitrogen management (2018-2021) Abzu – Advanced surface NMR instrumentation (2016-2019) Faster SNMR measurements with new receiver technology (2016-2017) Controlling Sound Zones signal processing (2015-2018)

• Biomedical Engineering
• Medical Imaging
• Nanoparticle Drug Delivery
• +۳ مورد دیگر

Thomas Nielsen is an Associate Professor at the Department of Electrical and Computer Engineering, Aarhus University, specializing in Biomedical Engineering. His research bridges medical instrumentation development and clinical oncology applications, with particular expertise in diagnostic imaging technologies. Research Focus: Medical imaging innovation including advanced MRI techniques (hyperpolarized 13 C, 19 F, DCE-MRI) and photoplethysmography for cancer diagnostics Nanoparticle engineering for targeted drug delivery and contrast enhancement Tumor microenvironment analysis focusing on vasculature, hypoxia, and metabolic pathways Development of theranostic platforms combining diagnostics and therapeutics Publication Analysis: His recent work (2013-2022) demonstrates consistent focus on oncology imaging, with 73% of publications involving cancer diagnostics. Key methodological themes include nanoparticle-based contrast agents (33%), MRI technique development (40%), and photoplethysmography applications (13%). Research outputs frequently involve interdisciplinary collaboration across engineering, materials science, and clinical oncology. Projects: Directed three major research initiatives: F127-Folate coated SPION for cancer detection (2014) Chitosan coated PLGA PFOB nanoparticles for cell migration tracking (2013-2014) PLGA-PEG-Folate multimodal nanoparticles for combined MRI/NIR imaging and chemotherapy (2011-2014) Laboratory Affiliations: Conducts research through the Biomedical Engineering laboratories within the Department of Electrical and Computer Engineering, utilizing facilities for nanoparticle synthesis, optical imaging, and magnetic resonance experimentation.

Ioana O. Berceaمشاهده پروفایل

استادیار

• Data Structures
• Randomized Algorithms
• Approximation Algorithms
• +۱ مورد دیگر

Ioana O. Bercea is a tenure-track Assistant Professor in the Computer Science Department at KTH Royal Institute of Technology, affiliated with the Division of Theoretical Computer Science. She holds a PhD in Computer Science from the University of Maryland (advised by Samir Khuller), a Master's from the same institution (advised by Aravind Srinivasan), and a BS in Mathematics and Computer Science from the University of Chicago. Her research focuses on Data Structures (dictionaries, Bloom filters), Randomized Algorithms (hashing, clustering), and Computational Geometry (Traveling Salesman Problem). Her recent publications emphasize scalable data structures, dynamic filters, and algorithmic efficiency, with applications in database systems and high-dimensional optimization. Awards & Honors: Best Artifact of ACM SIGMOD 2023 Future Digileader (Digital Futures, 2023) VR Starting Grant (2024) She advises PhD student Jonas Østergaard Klausen (University of Copenhagen) and collaborates with research groups including BARC (Copenhagen) and Digital Futures. Her VR Starting Grant funds the project "DataTech: Techniques for scalable data storage and analysis."

• Programming Languages
• Formal Logic
• Software Verification
• +۵ مورد دیگر

Jesper Bengtson is a Lecturer in Theoretical Computer Science specializing in Programming Logic and Semantics at the IT University of Copenhagen. His research primarily focuses on formal methods for programming languages, with particular expertise in session types, separation logic, and proof assistants. Dr. Bengtson's research interests center around programming language theory and formal verification. His work explores how to mathematically verify software correctness through logical frameworks. He specializes in session types for communication protocols, separation logic for reasoning about concurrent programs, and developing tools for interactive theorem proving. His research bridges theoretical computer science with practical applications in software verification. His publication record shows consistent contributions to programming language theory over the past decade, with a clear trend toward applying formal methods to concurrent and distributed systems. Recent work demonstrates increasing focus on mechanized verification of session type systems and integration of session types with separation logic for concurrent program verification. Dr. Bengtson has been involved in several significant research projects including MECHANIST (MECHANIsation of Session typEs, 2021-2025), ToMeSo (Tools and Methods for Scalable Software Verifications, 2009-2013), and a general-purpose framework for software verification (2013-2017). These projects were funded by major Danish research foundations including Danmarks Frie Forskningsfond and Independent Research Foundation of Denmark. His work has been featured in media outlets, including a 2015 article titled 'Danish researcher wants to eradicate software errors' and a 2021 piece about 'Natural or woke? Big shooter introduces controversial soldier,' indicating public engagement with his research.

Veronika Cheplyginaمشاهده پروفایل

استاد

• Machine Learning
• Medical Image Analysis
• Pattern Recognition
• +۴ مورد دیگر

Veronika Cheplygina is a Professor at the IT University of Copenhagen and leads the Data-intensive Systems and Applications research group within the Pattern Recognition Revisited lab. Her work focuses on Machine Learning , Medical Image Analysis , and Pattern Recognition , with particular emphasis on Open Science practices and Bias in Medical AI . Primary Affiliation: Department of Data Science and Artificial Intelligence Research Metrics: 20 publications, 2 projects (2021-2025), 2 datasets Her research addresses critical challenges in medical imaging datasets, including annotation bias , data reuse , and fairness in AI through active collaboration with institutions across 5 countries. She has contributed to major conferences like NeurIPS and MICCAI. Scientific Funding & Recognition: Principal Investigator (PI) for Making Metadata Count (MMC) (Danmarks Frie Forskningsfond, 2022-2025) PI for Choosing a Transfer Source for Medical Image Classification (CATS) (Novo Nordisk Foundation, 2021-2025) She maintains an active role in academic governance as head of an assessment committee for faculty recruitment (2023) and has been featured in media discussions about medical AI practices.

Lars Duggenمشاهده پروفایل

پژوهشگر

• 3D Printing
• Piezoelectric Engineering
• Mechatronics
• +۳ مورد دیگر

Lars Duggen is a researcher and Head of Unit at SDU Mechatronics Center, University of Southern Denmark, specializing in advanced manufacturing, sensor systems, and mechanical design. His work bridges 3D printing, piezoelectricity, and industrial mechatronics. Institute of Mechanical and Electrical Engineering, University of Southern Denmark Head of Unit, SDU Mechatronics Research Focus: 3D printing of multi-material systems, piezoelectric gas sensors, microcantilever dynamics, and hybrid flexible electronics. Recent projects explore nonlinear sensor behavior, fault diagnosis algorithms, and biomedical applications like non-invasive glucose monitoring. Scientific Contributions: Key articles address machining forces in multi-material printing, functionalization layer modeling, and transfer learning for machinery diagnostics. His work has been cited in fields spanning mechanical engineering, materials science, and sensor technology. Honored with BHJ-priser (2022) Recognized in Sønderjylland research excellence (2022) Teaching & Supervision: Supervises mechatronics projects including fluid behavior modeling and mechanical component design. Collaborates on EU-funded initiatives like EUROFusion BB RH facility and Medical Microtechnology.

• Battery Components
• Lattice-Boltzmann Method
• Nanoscale Engineering
• +۴ مورد دیگر

Martin Lautenschläger is an Associate Professor at the Institute of Mechanical and Electrical Engineering, University of Southern Denmark (SDU Mechatronics), specializing in computational modeling and simulation of advanced engineering systems. His work bridges mechanical engineering and material science through high-performance computing, focusing on battery technologies, fluid dynamics, and nanoscale interactions. Academic Rank: Associate Professor Institution: University of Southern Denmark Research Focus: Battery microstructures, Lattice-Boltzmann methods, nanoscale lubrication, and multiphase flow simulations Research Trends: Recent publications emphasize pore-scale modeling of battery components, electrolyte filling processes, and multiscale approaches to optimize battery performance. His simulations span from molecular dynamics to continuum-level predictions, particularly in lithium-ion and lithium-sulfur systems. Collaborations: Active in interdisciplinary projects with institutions across Europe, focusing on energy storage, computational microstructure analysis, and manufacturing optimization. Teaching: Supervises master's and bachelor's theses in mechanical design, fluid dynamics, and battery technologies, while teaching core courses in mechanical engineering.

Hao Liمشاهده پروفایل

پژوهشگر ارشد

• Topology Optimization
• Mechanical Engineering
• Thermal Systems
• +۲ مورد دیگر

Hao Li is a MSCA Postdoc Fellow and Visiting Scholar at LIP6 , affiliated with the University of Southern Denmark in the Department of Mechanical Engineering . His research focuses on advanced computational methods for topology optimization in thermal, fluid, and structural engineering systems. Education: Not explicitly stated in the provided text. Current Projects: Leading EU-funded research on heat exchanger design using multiscale models and machine learning. Dr. Li's work spans multiscale topology optimization, level-set methods, and fluid-structure interaction, with applications in microchannel cooling, compliant mechanisms, and biodegradable composites. His recent publications highlight advancements in 3D conjugate heat transfer, adaptive meshing, and eigenfrequency maximization. The trends in his research output (2017–2025) emphasize thermal-fluid systems , high-resolution structural optimization , and manufacturable composite designs . Notable subfields include triply periodic minimal surfaces for cooling channels, nonlinear buckling analysis, and phasor-based dehomogenization techniques. Teaching & Supervision: Currently supervising projects on topology optimization frameworks for heat sinks and high heat flux cooling. His past projects (2018–2023) include research on piezoelectric transducers and thermal-fluid system design. Labs & Collaborations: Collaborates with institutions in Japan and France, focusing on experimental validation and industrial applications. His network includes partnerships with researchers in structural mechanics, computational fluid dynamics, and additive manufacturing.

Ivar Lundمشاهده پروفایل

دانشیار

• Agricultural Engineering
• Precision Agriculture
• Spray Technology
• +۴ مورد دیگر

Ivar Lund is an Associate Professor at the University of Southern Denmark (SDU) in the Department of Industrial and Civil Engineering. His work focuses on agricultural engineering, precision spraying, and thermal energy systems with phase change materials. Education: B.Sc. in Engineering (Agro) from The Engineering College of Horsens (1982) Postgraduate courses at Royal Veterinary and Agricultural University (1999, 2006), University of Aalborg (2003-2004), and Ph.D. from University of Copenhagen (2008) His research spans robotic spraying systems for precision agriculture, fluid dynamics of droplet transport, and thermal energy storage using phase change materials (PCMs). Recent work includes experimentation and numerical modeling of SCR spray droplets, performance analysis of PCM-based building cooling systems, and confidence interval studies for spray measurement data. Key trends in his publications (2014-2023) include: reducing spray drift in pesticide application, optimizing urea-water solution distribution for automotive emissions control, and PCM integration in sustainable building systems. Collaborations highlight partnerships with institutions like UC Davis and University of Aarhus. Lund actively contributes to conferences, including presentations on spray drift modeling (2018), agricultural spray analysis (2018), and micro-jet herbicide application (2018). His work bridges engineering innovation with practical agricultural and energy efficiency solutions.

Torben Bach Pedersenمشاهده پروفایل

استاد

• Data Engineering
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Professor Torben Bach Pedersen at Aalborg University's Department of Computer Science within The Technical Faculty of IT and Design is a leading expert in Data Engineering, Artificial Intelligence, and Energy Systems. With over 394 publications and 19 completed projects, he directs research at the Daisy – Center for Data-intensive Systems and leads innovations in energy flexibility and smart grid technologies. Key research areas: Data Warehousing, AI/ML, Energy Systems, Smart Grids Major projects: domOS (Smart Building OS), FEVER (Virtual Power Plants), DiCyPS (Cyber-Physical Systems) His research spans data-intensive systems, AI applications in energy management, and smart infrastructure development. Recent work focuses on transformer-based network AI and energy flexibility metrics. Scientific recognition includes: Æresdoktor (Honorary Doctor) at TU Dresden (2021) Best Paper Award Runner-Up (2019) WWW 2017 Best Demo Award Best Poster Award World Smart Grid Forum (2013) Member of Danish Academy of Technical Sciences (2013) As principal investigator and supervisor in 17 PhD projects, he advances AI-driven solutions for 6G wireless systems, smart buildings, and energy market optimization.

John Stouby Perssonمشاهده پروفایل

دانشیار

• Information Systems
• Agile Software Development
• Pragmatism
• +۷ مورد دیگر

John Stouby Persson is an Associate Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design, specializing in Human-Centered Computing. His research focuses on practical problems experienced by software engineers, interaction designers, and IT professionals, with particular interest in agile development, user experience integration, and digitalization challenges. His research interests span information systems, agile software development, pragmatism, action research, digitalization, IT project management, and big data analytics. Persson approaches research as a collaborative practice of understanding and solving problems in organizing and leading system development, working with both public and private organizations. He has been particularly concerned with how information systems and digitalization are shaped by globalization, sustainability, value complexity and pragmatism. His recent publications show a strong trend toward the integration of AI tools in software development, UX practices, and addressing energy-related challenges through digital solutions. His work bridges practical software engineering concerns with theoretical frameworks, often using action research methodologies to develop solutions grounded in real-world contexts. Scientific Awards: Best paper award at the 12th Scandinavian Conference on Information Systems (SCIS 2021) Persson actively engages with both academic and public discourse, having participated in numerous conferences and provided expert commentary on digitalization initiatives including the Danish CoronaPass system. His consulting work demonstrates practical application of his research, particularly in realizing benefits from digitalization projects. His research aligns with UN Sustainable Development Goals, particularly those related to sustainable communities and responsible consumption.

Andreas Fischerمشاهده پروفایل

پژوهشگر

• Wind Turbine Engineering
• Aeroacoustics
• Leading Edge Erosion
• +۵ مورد دیگر

Andreas Fischer is a Senior Researcher at the Department of Wind and Energy Systems (Rotors Wind Turbine Design Division) at Technical University of Denmark (DTU). His work focuses on wind turbine aeroacoustics, leading edge erosion, and advanced computational fluid dynamics (CFD) simulations. Research Interests: He specializes in aeroacoustic noise sources, trailing edge modifications, and hybrid CAA (Computational Aeroacoustics) approaches. His research aligns with UN Sustainable Development Goals related to wind energy innovation. Current Projects: Principal Investigator for Sustainable and Cost Efficient Small Wind Turbine for Competitive Distributed Energy (2025–2027). Recent Publications: Contributions to wind tunnel experiments, digital twin development, and turbulence modelling for noise reduction. Supervision: Actively mentors PhD candidates like Pindi Nataraj and Nyborg C.M., integrating practical measurement campaigns with numerical simulations.

Frederik Zahleمشاهده پروفایل

پژوهشگر

• Wind Turbine Engineering
• Aerodynamics
• Aeroelasticity
• +۴ مورد دیگر

Frederik Zahle is a Senior Research Scientist at the Department of Wind and Energy Systems at the Technical University of Denmark (DTU). His work focuses on Wind Turbine Engineering , Computational Fluid Dynamics (CFD) , and Aeroelastic Stability Analysis , particularly in airfoil optimization, vortex shedding, and multi-fidelity modeling. He contributes to international collaborations such as the IEA Wind Tasks and leads projects on boundary layer control and large-scale turbine design. Key Research Areas : Aerodynamics, CFD, aeroelasticity, airfoil design. Supervision : He supervises PhD students like Sina Habibzadeh and Felipe Cespedes Moreno. Projects : Involved in initiatives like the Tip and Root Vortex Generators for Boundary Layer Control and the IEA 22-MW Reference Wind Turbine . Zahle's recent articles emphasize high-fidelity CFD , stabilized solvers , and offshore turbine optimization , reflecting trends in computational modeling and system engineering for wind energy. He actively participates in conferences and workshops, advancing aerodynamic design tools and validating complex flow simulations.

Sina Samarehمشاهده پروفایل

پژوهشگر

• Wind Energy
• Composite Materials
• Structural Health Monitoring
• +۴ مورد دیگر

Sina Samareh is a Tenure Track Researcher in the Department of Wind and Energy Systems at the Technical University of Denmark (DTU), specializing in structural design and testing of wind energy materials and components. Based at the Roskilde campus (Frederiksborgvej 399, Building 101, Room S04), his work focuses on the integrity and health monitoring of wind turbine structures through experimental and computational approaches. Dr. Samareh's research spans wind energy systems, composite materials, structural health monitoring, fatigue analysis, finite element modeling, and delamination mechanics. He investigates critical failure modes in wind turbine blades—including fatigue-induced delamination, crack propagation, and material degradation—using advanced techniques like thermography, acoustic emission, and element testing. His work addresses industry challenges in blade durability under cyclic loading and environmental stresses, with direct applications to improving turbine reliability and lifespan. Analysis of his publication trends (2020-2025) reveals consistent advancement in experimental methodologies for composite failure analysis, particularly in mixed-mode fracture, creep-fatigue interactions, and thermography-based damage detection. His research bridges computational modeling with physical validation, emphasizing scalable solutions for industrial wind energy applications. Key themes include fatigue delamination growth in thick laminates, rotor blade structural integrity, and novel test specimen designs for composite materials. Dr. Samareh actively contributes to academic discourse through peer review for journals including Engineering Fracture Mechanics and Measurement, and has presented at conferences such as the NAFEMs seminar on Modeling Dynamics, Fatigue and Fracture (2023) and the Risø International Symposium. His supervisor activities indicate involvement in mentoring students, though specific advisee details are not publicly documented. Within DTU, he operates in the Structural Design and Testing Wind Energy Materials and Components Division, which conducts cutting-edge research on material characterization, structural validation, and failure prediction for next-generation wind turbine systems. This division leverages DTU's wind energy testing facilities to address industry-critical challenges in large-scale composite structures.

Tomislav Dragicevicمشاهده پروفایل

استاد

• Power Electronics
• Applied Machine Learning
• Energy Systems
• +۵ مورد دیگر

Tomislav Dragicevic is a Full Professor at the Technical University of Denmark (DTU) , where he leads the Smart Converter Lab and coordinates the industry-anchored research initiative DRIVE-IT. He is an IEEE Fellow , Clarivate Highly Cited Researcher (2023 & 2024), and founder of PHLIT, a spinout commercializing grid services via intelligent power converters. Research Interests : His work focuses on power electronic converters and drives , digitalization , flexibility , and applied machine learning , enabling secure deployment of converter-dominated systems in EV charging, electrolyzers, water networks, and industrial drives. Article Trends : Recent publications highlight advancements in predictive control , neural networks for power electronics, ageing forecasting in semiconductors, virtual power plants , and EV charger-grid integration . Keywords span Power Electronics , Machine Learning , and Smart Grids . Awards : IEEE IES Early Career Award, Alexander von Humboldt Fellowship, Robert Mayer Energy Conservation Award, Končar Award for Best Industrial Ph.D. Thesis. Advising & Funding : Supervised 15 Ph.D. students and 6 postdocs, with €20 million+ in competitive funding, including the DFF Sapere Aude Research Leader project MAGIC and the HEART initiative for high-efficiency EV chargers. Labs & Teams : Founded the Smart Converter Lab at DTU and leads DRIVE-IT, a collaboration with Danfoss Drives. His group pioneered self-learning diagnostics for power converters and edge-deployed neural networks for predictive control.