جست‌وجوی استاد و پژوهشگر

• Molecular Biology
• Protein Biochemistry
• Plant Molecular Biology
• +۴ مورد دیگر

Charlotte O'Shea is an Academic employee at the Department of Biology, Biomolecular Sciences, University of Copenhagen. Her research focuses on molecular mechanisms in plant stress responses, protein structure and function, and computational approaches to protein design. Based at Ole Maaløes Vej 5 in Copenhagen, she maintains an active research program with numerous publications spanning biochemistry, molecular biology, and plant sciences. Her primary research interests include: Transcription factor regulation and protein-protein interactions in plants Mechanisms of plant stress responses to environmental challenges Protein folding, stability, and conformational dynamics Computational methods for protein design and engineering Molecular mechanisms of gene regulation through alternative splicing Dr. O'Shea's publication record demonstrates a strong interdisciplinary approach combining biochemical, biophysical, and computational methodologies. Her recent work has appeared in high-impact journals including Nature Communications, Nucleic Acids Research, and Journal of the American Chemical Society, with several papers receiving significant citations and attention in the scientific community. Her research trajectory shows consistent productivity with publications spanning from 2021 to the current year of 2025. Her scientific contributions have garnered attention across academic platforms, with multiple publications shared by X users and tracked by readers on Mendeley, indicating engagement with the broader research community. While specific awards are not documented in the provided information, the quality and impact of her work are evident through citations and continued publication in reputable journals.

• Machine Learning
• Deep Learning
• Remote Sensing
• +۵ مورد دیگر

Fabian Cristian Gieseke serves as an Associate Professor in the Department of Computer Science at the University of Copenhagen's Faculty of Science. His research focuses on applying machine learning techniques to environmental monitoring and remote sensing applications. He maintains an active research profile with significant contributions to the field of computational environmental science. Dr. Gieseke's research interests center on Machine Learning, Deep Learning, and their applications in Remote Sensing and Environmental Monitoring. His work particularly emphasizes forest resource monitoring, tree detection, and biomass estimation using advanced AI techniques. He has developed innovative approaches for analyzing satellite and aerial imagery to address critical environmental challenges related to forest management and climate change. His recent publications demonstrate a strong trend toward applying deep learning models to environmental monitoring problems, particularly in forest ecosystems. His research spans from national-scale tree counting and crown segmentation to above-ground biomass estimation using LiDAR data. His work shows increasing interdisciplinary collaboration between computer science and environmental science communities, with publications appearing in high-impact journals across both domains. Dr. Gieseke maintains an active research profile with 76 total research outputs as documented, including 41 article in proceedings, 24 journal articles, and various other publication types. His work has received significant attention with mentions across multiple social media platforms, news outlets, and policy documents, indicating real-world impact of his research. Based at the Department of Computer Science (DIKU) at the University of Copenhagen, Dr. Gieseke's research appears to be part of broader environmental informatics initiatives that bridge computer science with ecological monitoring and climate science applications.

• Deep learning
• Computer vision
• Remote sensing
• +۴ مورد دیگر

Dimitri Pierre Johannes Gominski is an active Assistant Professor in the Department of Geosciences and Natural Resource Management within the Faculty of Science at the University of Copenhagen. His research bridges computer vision and environmental science, with a focus on applying deep learning techniques to satellite imagery for environmental monitoring. He leads the C-Trees project aimed at mapping individual trees globally, with recent work spanning multiple continents including Europe, India, Rwanda, and the Peruvian Amazon. Education: PhD in Computer Vision (2018-2021) from École Centrale Lyon and IGN Paris MS in Electrical Engineering (2012-2017) from INSA Lyon Gominski's research focuses on making deep neural networks work in real-world conditions where perfect experimental setups are unavailable. His primary research interests include deep learning, generalization techniques, and image retrieval/detection/segmentation specifically applied to environmental monitoring. His work demonstrates expertise in adapting computer vision approaches for practical environmental applications, particularly in forest monitoring and land use analysis. Analysis of his recent publications (2024-2025) reveals a strong emphasis on using satellite imagery, particularly PlanetScope data, for environmental monitoring across diverse geographical contexts. His research shows interdisciplinary collaboration spanning computer science, environmental science, and geography, with applications in forest conservation, agricultural monitoring, and climate change mitigation. The publications demonstrate methodological innovation in domain adaptation for deep learning models applied to environmental monitoring challenges. Research Leadership: C-Trees project: Mapping individual trees from satellite imagery across Europe with plans for global expansion Active collaborations with researchers across multiple countries and institutions Gominski maintains an active research profile with multiple high-impact publications in 2024-2025 across journals including Nature Sustainability, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, and Communications Earth & Environment. His work has received attention from policy sources, news outlets, and academic communities as evidenced by references in policy documents, media coverage, and social media discussions.

• Plant Biochemistry
• Plant Genomics
• Systems Biology
• +۴ مورد دیگر

Marek Mutwil serves as an Associate Professor in the Department of Plant and Environmental Sciences at the University of Copenhagen, specializing in plant biochemistry with research spanning genomics, systems biology, and computational approaches to plant science. His work bridges experimental biology and data-intensive methodologies to address fundamental questions in plant environmental responses. His research program focuses on deciphering regulatory networks in plant stress adaptation, particularly through cross-species analyses of abiotic stress mechanisms in hydroponic systems. A significant portion of his recent work involves developing computational infrastructure for plant science, exemplified by the PlantConnectome knowledge graph that integrates literature-derived biological relationships across 71,000+ plant research articles. This dual emphasis on experimental stress physiology and bioinformatics resource development positions his work at the intersection of molecular plant biology and data science. Analysis of his 2025 publications reveals a consistent trajectory toward integrative plant systems biology, combining hydroponic crop stress experiments with large-scale knowledge graph applications. These works demonstrate growing emphasis on translational bioinformatics tools that convert fragmented plant science literature into structured, queryable biological networks. Scientific Awards: No awards were documented in the provided text. Advising and Grants: The source material contains no references to graduate students, postdoctoral trainees, or research funding sources. Labs and Teams: While affiliated with the Section for Plant Biochemistry, no specific laboratory structure, team members, or collaborative consortia are described in the available information.

Mario Lovricمشاهده پروفایل

پژوهشگر

• Food Microbiology
• Gut Health
• Fermentation
• +۳ مورد دیگر

Mario Lovric is a Guest Researcher in the Department of Food Science at the University of Copenhagen's Faculty of Science, specializing in Food Microbiology, Gut Health, and Fermentation. His research integrates machine learning approaches with biochemical and microbiological investigations across multiple interdisciplinary domains. His primary research areas include: Food Microbiology and Gut Health Metabolomics and Metabolite Bioactivity Machine Learning applications in Chemistry and Medicine Indoor Air Quality and Fungal Communities Early Childhood Inflammation and Developmental Outcomes Lovric's publication record demonstrates a consistent focus on applying advanced computational methods to solve complex problems in food science and microbiology. His recent work shows increasing emphasis on explainable AI techniques for analyzing biological systems, particularly in mother-child metabolome transfer and childhood health outcomes. The publications reveal strong international collaboration patterns across medical, biochemical, and computational disciplines. Several of Lovric's publications have received significant attention in the scientific community, with some picked up by multiple news outlets and widely shared on academic platforms like Mendeley. His work on molecular representation methods has accumulated 21 Scopus citations, indicating substantial impact in the field of chemical informatics.

Jan Støvringمشاهده پروفایل

پژوهشگر

• Landscape Architecture
• Urban Development
• Sustainable Stormwater Management
• +۵ مورد دیگر

Jan Støvring is a Senior Research Consultant at the Department of Geosciences and Natural Resource Management, University of Copenhagen, serving as Head of Studies for the Bachelor of Science program in Landscape Architecture since 2022. With a professional landscape architecture degree (MDL) and PhD completed between 2012-2017, he transitioned to academia in 2008 after six years of professional practice. His educational background includes a professional landscape architecture qualification (MDL) and doctoral studies culminating in a PhD awarded between 2012 and 2017. Støvring's research confronts urban development challenges through innovative design solutions targeting congestion and climate change impacts. He specializes in sustainable stormwater management with deep expertise in permeable pavements, circular economy implementation, and sustainable landscape creation. His work bridges theoretical research and practical application in urban resilience. Recent publications reveal strong interdisciplinary trends, integrating artificial intelligence into planting design, advancing circular material systems in urban paving, developing sophisticated stormwater modeling techniques, and pioneering film-based methodologies for landscape analysis and community engagement in projects like the Gels Å and Ribe Å river systems. No scientific awards were documented in the source materials. Støvring actively teaches undergraduate landscape architecture courses and supervises both bachelor's and master's students while coordinating internship programs for practical experience. His collaborative work includes the Ribe Å system pilot project (2021-2022) and participation in multiple landscape restoration initiatives across Danish river systems.

• Cancer Genomics
• Genome Instability
• DNA Repair
• +۷ مورد دیگر

Fran Supek is a Professor at the University of Copenhagen's Faculty of Health and Medical Sciences, where he leads the Genome Data Science laboratory at the Biotech Research & Innovation Centre (BRIC). He also maintains a research group at the IRB Barcelona. Supek is the Principal Investigator of ERC Starting and ERC Consolidator projects, an EMBO Young Investigator, and holds an ICREA tenured professor position (currently on leave). Dr. Supek obtained his PhD in Molecular Biology in 2010 from the University of Zagreb, followed by postdoctoral training at the Centre for Genomic Regulation (CRG) as a Marie Curie fellow. He established his independent laboratory at IRB Barcelona in 2017 as a "Ramón y Cajal" tenure-track fellow, and expanded to BRIC in 2024. His research focuses on statistical genomics and machine learning approaches to study genome stability mechanisms, particularly through analysis of mutation patterns in cancer. The Genome Data Science lab performs large-scale bioinformatic studies of multi-omic data from human tumors, populations, and metagenomes. Key research areas include DNA repair mechanisms, mRNA synthesis and turnover pathways, genetic interactions in cancer genomes, and machine learning approaches for inferring gene function from genomic data. Analysis of Supek's recent publications reveals a strong emphasis on cancer genomics, with particular focus on mutational signatures, DNA repair deficiencies, and the application of artificial intelligence to genomic data analysis. His work spans from fundamental mechanisms of genome instability to translational applications in cancer diagnosis and treatment. ERC Starting Grant recipient ERC Consolidator Grant recipient EMBO Young Investigator ICREA tenured professor (on leave) As Principal Investigator of multiple ERC projects, Supek leads a research program with significant funding for innovative genomic research. His laboratory at BRIC and IRB Barcelona conducts interdisciplinary research combining computational approaches with experimental validation to address fundamental questions in genome biology and cancer evolution. The Genome Data Science lab maintains active collaborations across multiple institutions, with research spanning statistical genomics, machine learning applications in bioinformatics, and experimental validation of computational predictions in cancer biology.

• Breast cancer screening
• Cervical cancer screening
• HPV vaccination
• +۵ مورد دیگر

George Napolitano is an Academic employee at the University of Copenhagen's Department of Public Health within the Section of Environmental Health. His work focuses on providing statistical support for various research projects in public health, with particular expertise in cancer screening programs and environmental health research. Dr. Napolitano holds a PhD in Mathematics from the Department of Mathematical Sciences and an MSc in Physics from the University of Naples Federico II. Prior to his current position, he worked as a Postdoc/Researcher at the Centre for Mathematical Sciences from 2013 to 2017, where he conducted research in Theoretical and Mathematical Physics and Applied Probability. His primary research interests include breast cancer screening, cervical cancer screening, HPV vaccination, and allostatic load. His expertise in statistical analysis bridges advanced mathematical methodologies with practical public health applications. Dr. Napolitano has published extensively, with 51 research outputs documented in his profile. His recent publications (2024-2025) demonstrate a strong focus on cervical cancer screening outcomes among HPV-vaccinated women through the Trial23 cohort study, breast cancer screening methodologies including tomosynthesis and deep learning approaches, and environmental health research examining air pollution's impact on lung cancer, respiratory infections, and COVID-19 mortality. His work consistently applies sophisticated statistical methods to address critical questions in epidemiology. Dr. Napolitano maintains an active research profile with multiple publications receiving significant attention from news outlets and academic platforms. His interdisciplinary background in theoretical physics and mathematics provides unique expertise in developing and applying complex statistical models to health data, making substantial contributions to cancer epidemiology and environmental health research.

• Plant Physiology
• Weed Science
• Agricultural Phenotyping
• +۳ مورد دیگر

Guy Robert Yeoman Coleman is a Research Fellow in the Department of Plant and Environmental Sciences (Section for Crop Sciences) at the University of Copenhagen, focusing on plant physiology and agricultural technology innovation. His work bridges field-based phenotyping and computational approaches to crop science challenges. Key research interests include: Stomatal anatomy and physiology phenotyping using rapid field-deployable tools like FieldDino Image-based weed recognition systems and evolutionary implications for crop mimicry Integration of robotics and AI for precision agriculture applications His recent publications highlight trends toward real-time, in-field data acquisition and analysis, emphasizing practical solutions for sustainable crop management. Scientific contributions center on methodological advancements in agricultural imaging and evolutionary weed science, with no awards or major grants explicitly documented.

• Geosciences
• Natural Resource Management
• Land Cover
• +۳ مورد دیگر

Konrad Ireneusz Turlej serves as a Postdoctoral Researcher and Guest Researcher at the Department of Geosciences and Natural Resource Management, University of Copenhagen, focusing on interdisciplinary studies in geography, land systems, and environmental monitoring. His research centers on geospatial analysis and environmental change, with core expertise in: Land cover dynamics and classification Satellite remote sensing methodologies Natural resource management systems Global environmental dataset development Time-series analysis of Earth observation data Turlej's work exemplifies cutting-edge environmental informatics, particularly through his 2023 publication establishing a comprehensive global land cover training dataset spanning 36 years. This research bridges remote sensing technology with practical applications in climate impact assessment and sustainable resource governance, demonstrating methodological innovation in handling large-scale geospatial data.

Søren Ingvor Olsenمشاهده پروفایل

دانشیار

• Computer Vision
• Image Analysis
• Pattern Recognition
• +۳ مورد دیگر

Søren Ingvor Olsen is an Associate Professor in the Department of Computer Science at the University of Copenhagen's Faculty of Science. His research focuses on Image Analysis, Computational Modelling, and Geometry within the broader field of Computer Science. He maintains an active research profile with numerous publications and collaborations across various application domains. Dr. Olsen received his academic training at the University of Copenhagen, where he earned both his Master's degree in 1984 and his PhD in 1988, both from the Department of Computer Science. His academic career at the same institution began as an Assistant Professor from 1988-1991, followed by his promotion to Associate Professor. He served as Head of the Department from 1996-1999 and has held various significant roles including Visiting Professor at University Blaise-Pascal in France (2006) and involvement with the E-Science Center at the Faculty of Science (2008). Professor Olsen's primary research areas span 3D Computer Vision, Image Analysis, and Pattern Recognition. His work is characterized by its interdisciplinary nature, combining theoretical foundations with practical applications. Recently, he has focused on applying computer vision techniques to real-world problems such as railway sign detection, agricultural weed mapping using drone technology, and automation in food processing industries. His research bridges the gap between fundamental computer vision algorithms and their implementation in diverse industrial settings, demonstrating strong translational research capabilities. Analysis of his recent publication record reveals a strong trend toward applying computer vision and machine learning techniques to environmental monitoring and precision agriculture. His work demonstrates expertise in drone-based imaging systems, deep learning for object detection, and the development of practical solutions for agricultural and transportation infrastructure challenges. The interdisciplinary nature of his research is evident in collaborations spanning environmental science, agricultural engineering, and transportation systems. Throughout his career, Professor Olsen has contributed significantly to academic development, notably being responsible for the creation and accreditation of a cross-faculty bachelor's program in Science and IT in 2009. While specific grant information isn't detailed in the available materials, his extensive publication record and international collaborations suggest successful research funding throughout his career. His work appears to involve substantial student mentorship, though specific advisees aren't named in the provided information. Professor Olsen's research appears to be conducted through the Image Analysis group within the Department of Computer Science at the University of Copenhagen. His work with drone technology and agricultural applications suggests collaborations with agricultural research institutions and potentially industry partners in the railway and food processing sectors. The practical orientation of his recent work indicates strong connections between his academic research and real-world industrial applications.

Sizhuo Liمشاهده پروفایل

پژوهشگر ارشد

• Deep learning
• remote sensing
• forest management
• +۳ مورد دیگر

Sizhuo Li is a Postdoctoral Researcher at the Department of Geosciences and Natural Resource Management within the Faculty of Science at the University of Copenhagen. Working at the intersection of artificial intelligence and environmental science, Dr. Li applies advanced neural networks to solve critical challenges in forest management and carbon stock estimation using remote sensing technologies. Dr. Li's research interests center on developing and adapting deep learning methodologies for environmental monitoring applications. Their primary focus areas include intelligent forest management systems, tree resource quantification, and carbon stock estimation. Current research involves creating an automatic and scalable tree inventory framework based on UNet architecture that enables individual crown segmentation, precise tree counting, and accurate height estimation at country-wide scales. Dr. Li's publication record demonstrates a clear trajectory from technical deep learning methodologies to practical environmental applications across diverse geographical contexts, from the Mediterranean region to the Sahel and Rwanda. This work effectively bridges computer science and environmental science to address urgent climate change and sustainability challenges through innovative technological solutions. Dr. Li has published in prestigious journals including Nature Food, Nature Ecology and Evolution, Nature Reviews Electrical Engineering, and Frontiers in Remote Sensing, with multiple publications receiving significant citations and media attention. Their research has been highlighted by numerous news outlets and academic platforms, indicating growing influence in the field of AI-powered environmental monitoring.