جست‌وجوی استاد و پژوهشگر

• Statistics
• Applied Statistics
• Precision Agriculture
• +۳ مورد دیگر

Signe Marie Jensen is an Associate Professor in the Department of Plant and Environmental Sciences at the University of Copenhagen's Faculty of Science. Her research bridges statistical methodology with agricultural applications, focusing on precision agriculture and plant phenotyping. She leads innovative work applying computer vision and machine learning to solve critical agricultural challenges. Education: MSc in Mathematical Economics, University of Copenhagen (2007) PhD in Applied Statistics, University of Copenhagen (2015) Dr. Jensen's research interests center on statistical methods for simultaneous inference and analysis of phenotyping data. Her work has increasingly focused on applying advanced machine learning techniques, particularly YOLO algorithms, to agricultural challenges including pest detection, weed management, and plant phenotyping. She develops statistical frameworks that enable more accurate and efficient analysis of complex agricultural data. Her recent publications reveal a strong trend toward interdisciplinary research combining statistics, computer vision, and plant science. The majority of her 2024-2025 publications focus on applying YOLO-based object detection systems to agricultural problems, particularly in precision farming applications. These works demonstrate her ability to translate statistical theory into practical agricultural technologies that address food security and sustainable farming challenges. Dr. Jensen's research output shows strong international collaboration, with co-authors from multiple countries working on agricultural technology development. Her work on the PhenoLab platform represents a significant contribution to high-throughput plant phenotyping infrastructure. She maintains active research in both theoretical statistics (simultaneous inference methods) and applied agricultural technology development, demonstrating remarkable versatility across these domains. Her current work on deep learning applications for crop monitoring represents the cutting edge of precision agriculture research.

• Ophthalmology
• Neuro-ophthalmology
• Artificial Intelligence in Medicine
• +۳ مورد دیگر

Dan Milea serves as an Adjunct Professor in the Department of Clinical Medicine at the University of Copenhagen, specializing in Ophthalmology. He maintains a dual affiliation with SingHealth in Singapore, where he can be reached at dan.milea@singhealth.com.sg. His primary research address is at Blegdamsvej 3, 2200 København N, Denmark, indicating strong institutional ties to the University of Copenhagen campus. Dr. Milea's research program centers on the intersection of ophthalmology and artificial intelligence, with particular emphasis on neuro-ophthalmic disorders. His work spans optic nerve pathologies, medical imaging analysis, visual prosthetics, and diagnostic technology assessment. He has pioneered AI applications for detecting conditions like optic atrophy, central retinal artery occlusion, and pediatric papilledema through analysis of standard fundus photographs and optical coherence tomography. His publication record shows a clear trajectory toward developing clinically applicable AI diagnostic tools, with 73 research outputs documented. The BONSAI (Brain and Optic Nerve Study with Artificial Intelligence) Group represents his primary research vehicle, producing systems that can discriminate between arteritic and nonarteritic optic neuropathies and identify papilledema in pediatric patients using conventional imaging techniques. Dr. Milea's research impact is evident through widespread academic engagement - his work has been discussed across multiple X (Twitter) platforms, shared on Facebook, covered by news outlets, referenced on Bluesky, and read by researchers on Mendeley. This multi-platform visibility demonstrates significant influence within both specialized ophthalmology communities and broader medical AI research circles.

• Musculoskeletal Ultrasound
• Rheumatology
• Ultrasound-Guided Interventions
• +۳ مورد دیگر

Lene Terslev serves as a Clinical Professor at the Department of Clinical Medicine within the Faculty of Health and Medical Sciences at the University of Copenhagen. Her clinical and research activities are anchored in the Internal Medicine: Rheumatology section, where she specializes in musculoskeletal ultrasound applications for rheumatic diseases. Her research spans Musculoskeletal Ultrasound , Rheumatology , and Medical Education , with emphasis on diagnostic precision in conditions like rheumatoid arthritis, Sjögren's syndrome, and giant cell arteritis. She pioneers standardized training models for ultrasound-guided procedures and develops assessment tools for trainee competency in interventional techniques. Analysis of her 2025 publications reveals three dominant trends: (1) Advancement of robotic ultrasound for rheumatoid arthritis diagnostics (ARTHUR trial), (2) Exploration of high-frequency ultrasound for Sjögren's disease via lacrimal gland assessment, and (3) Integration of AI with vascular ultrasound in giant cell arteritis (GCA-US-AI project). Her work consistently bridges clinical innovation with educational frameworks.

• Medical Image Analysis
• Artificial Intelligence in Healthcare
• Machine Learning
• +۳ مورد دیگر

Lea Marie Pehrson is a Guest Researcher at the Department of Computer Science, Faculty of Science, University of Copenhagen, with clinical affiliation at Region Hovedstaden (Copenhagen University Hospital). Her work bridges artificial intelligence and medical imaging, focusing on developing computational tools for radiology and oncology applications. Her research interests include: Medical Image Analysis Artificial Intelligence in Healthcare Machine Learning for Radiology Computational Modelling of Tumors Natural Language Processing for Clinical Reports Deep Learning-based Decision Support Systems Recent publications reveal a concentrated effort in applying deep learning to solve clinical imaging challenges, particularly in tumor volume delineation for radiation therapy and multilingual processing of radiology reports. Her work emphasizes systematic validation of AI tools through inter-observer agreement studies and real-world clinical impact assessments, with strong focus on Danish healthcare contexts and thoracic imaging. Dr. Pehrson operates within the Image Analysis, Computational Modelling and Geometry research group at the University of Copenhagen, maintaining active collaboration with clinicians at Region H to translate AI research into practical clinical decision support systems.

• Digital Pathology
• Image Analysis
• Neoplasia
• +۴ مورد دیگر

Thomas Hartvig Lindkær Jensen serves as a Clinical Associate Professor at the Department of Clinical Medicine, University of Copenhagen, and is affiliated with the Biotech Research and Innovation Centre (BRIC). He also works as a senior consultant at the Pathology Department, Rigshospitalet. His academic background includes an MD degree obtained in 2005 and a PhD earned in 2009. Dr. Jensen is board-certified in both pathology and forensic pathology, with specialized clinical expertise as a member of the Thorax Team at the pathology department. His clinical work focuses on pulmonary, cardiac, and thymic diseases, providing the foundation for his research activities. His primary research interest lies in image analysis within digital pathology, where he has made significant contributions through the co-founding of QUAN10.AI, an all-round image analysis system for HE-slides used throughout the Capitol Region for both research and clinical applications. His research spans multiple disease areas with a particular emphasis on neoplasia and the extracellular matrix. Recent publications demonstrate his interdisciplinary approach, bridging computational methods with clinical pathology across pulmonary, cardiac, and oncological domains. Dr. Jensen's research program has secured substantial funding from multiple sources, including the BetaHealth innovation program, Innovation Fund Denmark, and the Danish Cancer Society. His work on QUAN10.AI has received support for applications in lung cancer and in vitro fibrosis models. He also serves on the scientific board of the Danish Cancer Research Fund, demonstrating his leadership in the cancer research community. Senior consultant at Pathology Department, Rigshospitalet Clinical Associate Professor at Department of Clinical Medicine, University of Copenhagen Co-founder of QUAN10.AI platform for digital pathology analysis Scientific board member of the Danish Cancer Research Fund Board-certified specialist in pathology and forensic pathology

Fintan McEvoyمشاهده پروفایل

استاد

• Veterinary Imaging
• Image Analysis
• Machine Learning
• +۵ مورد دیگر

Fintan McEvoy is a Professor at the University of Copenhagen's Faculty of Health and Medical Sciences, affiliated with the Department of Veterinary Clinical Sciences. His research focuses on advancing veterinary imaging through computational methods, particularly in diagnostic radiology for companion animals. Education: Holds multiple advanced qualifications including: MVB (Veterinary Medicine) PhD DVSc (Doctor of Veterinary Science) DVR (Diploma in Veterinary Radiology) DipECVDI (Diplomate of the European College of Veterinary Diagnostic Imaging) Research Focus: Leads innovation in veterinary diagnostics through: Development of AI-powered tools for canine hip dysplasia assessment Quantitative CT analysis of pulmonary and adipose tissues Machine learning applications for radiographic feature classification Computer vision solutions for automated disease screening Validation of diagnostic protocols in veterinary dentistry Publication Trends: McEvoy's recent work (2024-2025) demonstrates concentrated focus on artificial intelligence applications in veterinary orthopedics, particularly deep learning systems for automated assessment of canine hip dysplasia. His research trajectory shows consistent integration of computational methods with diagnostic imaging throughout his career. Professional Leadership: President of the European College of Veterinary Diagnostic Imaging (2024-2026) Regular presenter at academic conferences including talks on AI in disease screening

• Motor Control
• Neuroscience
• Rehabilitation Science
• +۴ مورد دیگر

Jessica Pingel is an Associate Professor in the Department of Neuroscience within the Faculty of Health and Medical Sciences at the University of Copenhagen, specializing in motor control research. Her work bridges neuroscience and rehabilitation science with a focus on movement disorders and therapeutic interventions. Her research interests center on motor control mechanisms in neurological conditions, particularly cerebral palsy, stroke, and spinal cord injury. She investigates biomechanical, physiological, and molecular aspects of muscle function using advanced techniques like synchrotron imaging, ultrasound, and molecular analysis. Her work extends to adaptive sports such as frame running and therapeutic exercise protocols for neurological rehabilitation. Analysis of her recent publications (2022-2024) reveals three dominant research trajectories: (1) Cerebral palsy rehabilitation through frame running capacity studies and molecular biomarker analysis, (2) Advanced imaging of neuromuscular tissue atrophy in stroke/SCI, and (3) Methodological innovations in muscle measurement techniques and academic supervision models. Her interdisciplinary approach integrates human and animal models with clinical applications. Dr. Pingel actively supervises master's thesis students as evidenced by her publication on optimized supervision models. She participates in major research forums including the European Muscle Conference and is affiliated with the Nielsen Lab at the University of Copenhagen, where she contributes to translational neuroscience research.

Jalil Boudjadarمشاهده پروفایل

دانشیار

• Cyberphysical Systems
• Digital Twins
• Embedded Systems
• +۸ مورد دیگر

Jalil Boudjadar is an Associate Professor at the Department of Electrical and Computer Engineering, Aarhus University. His research focuses on cyberphysical systems, embedded systems, and AI optimization with applications in automotive, robotics, and energy systems. Cyberphysical Systems & Digital Twins Embedded AI & FPGA Acceleration Real-Time Systems & Dynamic Scheduling Formal Methods for Safety/Security Current projects include ACCURATE (resilient manufacturing through digital twins, 2024-2026), DigiGlass (sustainable glass fiber manufacturing, 2023-2024), and Embedded AI research (2022-2025). His work bridges hardware/software co-design, machine learning, and industrial sustainability across domains like automotive, robotics, and smart manufacturing.

Jan Hartvigsenمشاهده پروفایل

استاد

• Low Back Pain
• Chiropractic
• Neck Pain
• +۵ مورد دیگر

Jan Hartvigsen is Professor and Head of Research Unit at the Research Unit of Clinical Biomechanics within the Department of Sports Science and Clinical Biomechanics at the University of Southern Denmark. His research focuses on musculoskeletal disorders, particularly spinal pain, chiropractic care, and rehabilitation science. He leads multiple research projects including investigations into percutaneous vertebroplasty, lumbar spinal stenosis management, and shoulder pain in wheelchair users. Hartvigsen's research interests span low back pain mechanisms, neck pain epidemiology, systematic review methodology, spinal biomechanics, and non-pharmacological pain management. His work integrates clinical practice with public health perspectives to develop evidence-based rehabilitation approaches. His recent publications demonstrate a strong focus on spinal health outcomes, telehealth competencies, rehabilitation metrics, and interdisciplinary pain management. Research trends show increasing emphasis on digital health solutions, WHO guideline development, and population-level interventions for musculoskeletal disorders. Awards and Honors: Kiropraktorprisen 2017 David Chapman-Smith Honor Award (2017) WFC David Chapman-Smith Honor Award (2017) George B MaClelland Researcher of the Year (2017) George B McCelland Researcher of the Year - Americal Chiropractic Association (2017) Hartvigsen actively supervises graduate students and leads multiple funded projects including studies on spinal stenosis management, vertebral compression fractures, and wheelchair-related musculoskeletal disorders. He directs the Research Unit of Clinical Biomechanics and collaborates internationally on WHO rehabilitation initiatives.

Lars Jelsbakمشاهده پروفایل

استاد

• Plant beneficial microbes
• Microbiome engineering
• Microbial secondary metabolites
• +۳ مورد دیگر

Lars Jelsbak is a Professor at the Department of Biotechnology and Biomedicine, Technical University of Denmark. His research focuses on plant beneficial microbes, microbiome engineering, and the molecular mechanisms of antibiotic-resistant Pseudomonas aeruginosa. He leads projects addressing sustainable agriculture through microbial solutions and combating antimicrobial resistance. Jelsbak supervises multiple PhD students and collaborates internationally on synthetic microbiome research and pathogen genomics. Research Interests: Jelsbak’s work centers on engineering microbiomes to reduce agrochemical use and understanding Pseudomonas aeruginosa’s role in hospital infections. Key areas include secondary metabolite production in soil microbiomes and the evolution of high-risk bacterial clones. Publications: His recent work explores mutator phenotypes in Mycobacterium tuberculosis drug resistance (2025), biofilm-based medical device testing (2025), and Pseudomonas aeruginosa’s pathogenic adaptations (2024–2023). Research highlights include synthetic community standards and antimicrobial resistance dynamics in clinical settings. Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: Jelsbak supervises PhD projects on hydrogel biofilm combat, microbiological test methods for medical devices, and plasmid persistence in Aeromonas. He collaborates with institutions like Novo Nordisk and global research networks. Labs/Teams: Affiliated with DTU Microbes Initiative and Center for Microbial Secondary Metabolites, focusing on microbial interactions and synthetic biology applications.

Søren Dalsgård Petersenمشاهده پروفایل

پژوهشگر ارشد

• Synthetic Biology
• Metabolic Engineering
• Protein Expression
• +۴ مورد دیگر

Søren Dalsgård Petersen is a Research Fellow at the Department of Biotechnology and Biomedicine, Technical University of Denmark (DTU), specializing in synthetic biology and metabolic engineering. His work focuses on integrating computational tools with experimental approaches to advance bioengineering and biotechnology. He holds a PhD from DTU, where he contributed to projects such as 'Characterized Parts Libraries & Pathway Evolver' and 'PAcMEN: Predictive and Accelerated Metabolic Engineering Network.' His research interests span synthetic biology, microbial interactions, protein quality control, and applications of machine learning in agriculture and biotechnology. Notable contributions include developing open-source software like StreptoCAD for genome engineering and teemi for iterative design-build-test-learn cycles. Petersen has collaborated internationally on topics ranging from fungal biocontrol to plant growth optimization using spectral reflectance and weather data analysis. His publications highlight interdisciplinary approaches, including deep learning for fungal interaction classification, rapid plant screening assays, and predictive modeling of metabolic pathways. Petersen actively participates in academic activities, such as presenting at the DTU Open Research Day in 2017 and contributing to EU-funded networks like PAcMEN. His work emphasizes translational research, aiming to bridge computational predictions with real-world biotechnological applications. Key projects include metabolic engineering of eukaryotes, optimization of tryptophan biosynthesis in yeast, and functional mining of transporters. Petersen's lab focuses on systems biology methodologies, combining mechanistic models with machine learning to tackle challenges in sustainable bioproduction and environmental remediation.

Rasmus John Normand Frandsenمشاهده پروفایل

دانشیار

• Synthetic Biology
• Microbial Metabolism
• Fungal Genetics
• +۳ مورد دیگر

Rasmus John Normand Frandsen is an Associate Professor at the Department of Biotechnology and Biomedicine, Technical University of Denmark (DTU). He leads research in synthetic biology and microbial metabolism, focusing on fungal genetics and metabolic engineering. His work contributes to the UN Sustainable Development Goals related to sustainable agriculture and innovation. Frandsen is affiliated with the Section for Synthetic Biology and DTU Microbes Initiative. He holds a PhD in Biotechnology and has expertise in microbial engineering, automated laboratory systems, and AI-driven biological analysis. His research integrates robotics, deep learning, and genetic engineering to advance biotechnological applications. Key research areas include fungal secondary metabolite biosynthesis, microbial interaction networks, and high-throughput screening methods. He has developed novel robotic platforms for Streptomyces conjugation and AI-based systems for fungal identification and lab quality control. Recent projects involve metabolic pathway engineering in fungi for biofuel production and automated analysis of plant-microbe interactions. Frandsen supervises multiple PhD projects focusing on microbial systems biology, deep learning in genomics, and actinomycete engineering. His work emphasizes translational research with applications in biotechnology, agriculture, and environmental science.

Malte Pedersenمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Underwater Imaging
• Marine Organism Identification
• +۵ مورد دیگر

Malte Pedersen is a Postdoctoral Researcher at Aalborg University's Department of Architecture, Design and Media Technology within The Technical Faculty of IT and Design. He works in the Section for Media Technology at Campus Aalborg, focusing on visual analysis and perception with applications in marine and maritime research. His work is associated with several research initiatives including "Visual Analysis and Perception," "AI for the People," and "AAU BLUE – Marine & Maritime Research." Dr. Pedersen's research spans the intersection of computer vision and marine science, with a particular emphasis on underwater imaging and analysis. His work addresses challenges in turbid water environments, marine organism identification, and fisheries monitoring. He has developed several important datasets for underwater computer vision research, including BrackishMOT for multi-object tracking of marine species and 3D-ZeF for zebrafish tracking. His recent publications demonstrate a strong focus on applying computer vision techniques to solve practical problems in marine environmental monitoring, fisheries management, and underwater exploration. His research shows a clear trajectory toward developing robust AI systems that can operate effectively in challenging underwater conditions, with applications ranging from seagrass mapping to bycatch identification and automated fisheries monitoring. Computer Vision for underwater environments Marine organism re-identification Benthic habitat mapping Fisheries monitoring and management Object tracking in turbid water Underwater image processing Dr. Pedersen has secured significant research funding as Principal Investigator on multiple projects including FDF (Fully Documented Fisheries, 2024-2026), JAMBO (investigating seabed conditions in Jammerbugt, 2023-2024), AUTOFISK (automated monitoring of cod catch, 2022-2023), and Marine Analytics using Computer Vision (2020-2023). His work has contributed to marine research that has informed policy decisions, including research that "Paves the Way for a Ban on Beam Trawling."

Jakob Schiøtzمشاهده پروفایل

استاد

• Nanoparticles
• Molecular Dynamics
• Density Functional Theory
• +۳ مورد دیگر

Jakob Schiøtz is a Professor at the Department of Physics, Technical University of Denmark (DTU), leading the theory group in the Center for Individual Nanoparticle Functionality (CINF). His research focuses on nanoparticle shape-functional relationships, hot-electron catalysis, and nanocrystalline materials' mechanical properties. He coordinates the Master's program in Physics and Nanotechnology and has advised numerous PhD students. Education includes a PhD in Physics (DTU, 1992–1995), M.Sc. in Electronics Engineering/Applied Physics (DTU, 1986–1991), and postdoctoral experience at Washington University (1995–1996). He holds teaching qualifications in research management and didactics. Research interests span nanomaterials, catalytic processes, and computational methods like molecular dynamics and density functional theory. His work contributes to sustainable energy solutions through advanced material characterization and nanotechnology. Key grants include a Danish National Research Foundation Center (2005–2010) and collaborations with industry partners. Awards include the P. Gorm-Petersen Memorial Award (1997). Schiøtz maintains the open-source ASAP molecular dynamics software and actively reviews for top journals. His lab focuses on in situ electron microscopy, machine learning for image analysis, and nanoscale material dynamics. Projects involve catalytic nanoparticles, defect formation in 2D materials, and thermal transport phenomena.

Esben Pilgaard Skovsenمشاهده پروفایل

دانشیار

• Physics
• Optics
• Spectroscopy
• +۱۲ مورد دیگر

Esben Pilgaard Skovsen is an Associate Professor in the Department of Materials and Production at Aalborg University, Faculty of Engineering and Science. His research is centered on terahertz technology, optics, and spectroscopy, with applications in material characterization, remote sensing, and security. He is actively involved in leading research projects and has a strong record of scientific output and collaboration. Education: PhD in Chemistry (Time-Resolved Spectroscopy), Aarhus University (Awarded 20 Dec 2005) M.Sc. in Physics, Aarhus University (Awarded 7 May 2002) Postdoctoral position, Aarhus University (Oct 2005 – Nov 2005) His research interests span terahertz spectroscopy , nonlinear optics , femtosecond laser physics , and material analysis . He has pioneered work in standoff detection of explosives (DETRIS project), continuous-wave terahertz systems , and novel dosimetry using polymers . His recent publications show a trend toward integrating machine learning for spectral classification and improving terahertz source efficiency. His work often bridges fundamental physics with engineering applications. Scientific Awards and Recognition: No formal awards listed, but recognized through media coverage and research funding. Skovsen actively supervises student projects and contributes to academic service through peer review (e.g., for Sensors journal), internal and external examinations, and consultancy (e.g., for the German Research Council). He has led multiple research grants, including industry collaborations and national projects. He is a Principal Investigator on projects such as DETRIS and CITS, focusing on compact terahertz systems and explosive detection. He also contributes to open science through public datasets on Zenodo. His lab work emphasizes experimental terahertz setups, purge systems, and fiber-coupled instrumentation for improved stability and portability.