جست‌وجوی استاد و پژوهشگر

Mogens Fosgerauمشاهده پروفایل

استاد

• Transport Economics
• Discrete Choice Modeling
• Travel Behavior
• +۷ مورد دیگر

Mogens Fosgerau is a Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), where he conducts research in transport policy and transportation science. His work spans econometrics, travel behavior modeling, and transport economics, contributing to sustainable urban mobility and policy design. Institution: Technical University of Denmark Department: Department of Technology, Management and Economics Email: mogens.fosgerau@econ.ku.dk ORCID: https://orcid.org/0000-0002-6452-5215 His research focuses on discrete choice modeling, travel time valuation, scheduling preferences, and congestion pricing. He develops theoretical and empirical models to understand how individuals make travel decisions under uncertainty and how these behaviors affect urban transport systems. His work integrates economic theory with data-driven methods, often using large-scale datasets and advanced econometric techniques. The recent trend in his publications highlights innovations in perturbed utility models for route choice, stochastic traffic assignment, and the analysis of induced demand for cycling. His research bridges transportation science, behavioral economics, and operations research, with applications in urban planning and policy evaluation. Scientific awards received include: The International Choice Modeling Conference (ICMC) award for Most Innovative Application (2022) Best Overall Paper Award, ITEA Conference (2015) Best Paper Awards from BIVEC-GIVET (2007), Kuhmo-Nectar (2008) Hedorfs Fonds Pris for Transportforskning (2011) Mogens Fosgerau has supervised PhD students such as Fentie Abegaz and has been involved in multiple externally funded research projects, including URBAN (Innovation Fund Denmark), IRUC (Danish Council for Strategic Research), and Horizon 2020 initiatives. He has also served on review panels, including for the Norwegian Research Council, and contributed to peer review and editorial duties. He is actively engaged in research networks and has presented his work at international conferences. His projects often involve interdisciplinary collaboration with researchers in economics, engineering, and urban planning.

Gregor Giebelمشاهده پروفایل

پژوهشگر

• Wind Power Forecasting
• Renewable Energy Integration
• Wind Farm Flow Control
• +۷ مورد دیگر

Dr. Gregor Giebel is a senior researcher and Head of Section in the Department of Wind and Energy Systems at the Technical University of Denmark (DTU), specifically within the Renewable Plants in Energy Systems group. He is actively engaged in advancing wind energy technologies and their integration into modern power systems. His leadership extends to major international and EU-funded initiatives, including IEA Wind Task 51 and the NEST Facilities research infrastructure. Gregor Giebel's research focuses on short-term wind power forecasting , large-scale integration of wind into electricity grids , wind farm flow control , and condition monitoring of wind turbines . His work is instrumental in improving the reliability and efficiency of renewable energy systems. He has made significant contributions to international standardization through the IEC and is a key figure in global wind energy research coordination. The recent publications highlight a strong trend toward weather-driven energy forecasting , modeling wind farm impacts on weather systems , and developing national research infrastructures for energy transition . These works reflect a deep integration of meteorology, energy systems engineering, and policy-relevant research. His scientific recognition includes: Poster Award at the WindEurope Offshore Conference, Copenhagen 2019 Poster Award on WindEurope Conference, Copenhagen 2021 Dr. Giebel is a Principal Investigator (PI) on multiple high-impact projects such as NEST Facilities , IEA Wind Task 51 , and GreenHyScale . He is renowned for his success in research funding, with a proposal success rate exceeding 50%. He has led the EU Marie Curie Initial Training Network Train 2 Wind and previously headed the FarmConners EU Coordination Action on wind farm control. He leads and is deeply involved in the NEST Facilities —a distributed Danish research infrastructure for energy transition—and contributes significantly to the Weather2X and GreenHyScale projects, which aim to advance green hydrogen production and weather forecasting for energy systems.

Andreas Bjerre-Nielsenمشاهده پروفایل

دانشیار

• Educational Public Policy
• AI for Public Policy
• Social Networks
• +۶ مورد دیگر

Andreas Bjerre-Nielsen is an Associate Professor at the Department of Economics and Copenhagen Center for Social Data Science (SODAS) within the Faculty of Social Sciences at the University of Copenhagen. His work bridges economics and data science to analyze education-related behavior and policies. Research Focus: School choice, digital technology in education, predictive analytics for interventions, and social network effects. Methodology: Combines econometrics with machine learning techniques to evaluate policy impacts. Research Trends: Recent publications emphasize algorithmic fairness in college admissions, socioeconomic impacts of school boundary policies, and behavioral insights from large-scale datasets. His 2025 Scientific Reports study reveals nation-scale social network dynamics. Awards and Grants: Tietgen Prize (2021) for young social science researchers 2024: Independent Research Fund Denmark grant for 'Coded Clues' project 2023: Major grant for school choice research Collaborations: Works with Danish Ministry of Children and Education through UDDanKvant unit, and collaborates with multidisciplinary researchers including Sune Lehmann and David Dreyer Lassen.

Peter Behrensdorff Poulsenمشاهده پروفایل

پژوهشگر

• Solar Cell Engineering
• Photovoltaics
• Drone Engineering
• +۵ مورد دیگر

Peter Behrensdorff Poulsen serves as Solar Photovoltaic Systems Group Leader at the Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU). His work spans photovoltaic research, solar-powered lighting systems, and drone-based inspection technologies within DTU's College of Engineering. His research focuses on Solar Cell Engineering , Photovoltaic Modules , and Drone Applications for renewable energy systems. Key areas include bifacial photovoltaics, electroluminescence imaging diagnostics, building-integrated photovoltaics, and ultra-efficient solar-powered lighting solutions. His work significantly contributes to UN Sustainable Development Goals related to affordable clean energy and sustainable cities. Recent publications reveal strong trends in AI-driven PV diagnostics , bifacial module performance in northern climates, and dark-sky compatible lighting systems . His research bridges fundamental photovoltaic science with practical applications in urban infrastructure and environmental sustainability. Best Poster Award at 44th IEEE Photovoltaic Specialists Conference Best Poster Award at 48th IEEE Photovoltaic Specialists Conference Characterizing the Performance of Daylight Filters for Electroluminescence Imaging Poster Award at 35th European Photovoltaic Solar Energy Conference Poster Prize at Sustain 2017 Poulsen leads multiple research grants including the Ultra-efficient Dark Sky-compatible Solar-powered Outdoor Lighting project (2024-2026) and previously managed the DronEL project (2017-2019) for drone-based PV inspection. His work connects photovoltaic engineering with practical lighting applications through the Lighting Color and Radiation Laboratory. He actively collaborates with industry partners on building-integrated photovoltaics and solar-powered infrastructure solutions, with notable projects including the Plateau Sun Hub public charging stations and Black Si BIPV panel development.

Schahram Dustdarمشاهده پروفایل

استاد

• Distributed Systems
• Cloud Computing
• Services Computing
• +۵ مورد دیگر

Schahram Dustdar is a Full Professor of Computer Science and head of the Distributed Systems Group at TU Wien (Vienna University of Technology), Austria. He has held significant international academic positions, including Honorary Professor at the University of Groningen (2004–2010) and Visiting Professor at the University of Seville (Dec 2016–Jan 2017) and UC Berkeley (Jan–Jun 2017). His research interests lie at the intersection of distributed computing, cloud services, and intelligent data systems. He actively contributes to advancing the fields of services computing, cloud infrastructure, web technologies, and data-driven financial modeling. His work emphasizes scalable, robust, and knowledge-aware systems, particularly in financial data visualization and transaction network analysis. The most recent publications highlight his focus on modeling financial transaction networks using constraint satisfaction and developing visualization frameworks that incorporate incremental domain knowledge. These works reflect a strong trend toward integrating formal methods with interactive data systems for enterprise and financial applications. ACM Distinguished Scientist (2009) IBM Faculty Award (2012) IEEE Fellow (2016) Elected Member of Academia Europaea Schahram Dustdar has supervised multiple research projects and leads a vibrant research group at TU Wien. He has been involved in editorial leadership as Editor-in-Chief of Computing (Springer) and Associate Editor for top-tier journals such as IEEE Transactions on Cloud Computing, IEEE Transactions on Services Computing, ACM Transactions on the Web, and ACM Transactions on Internet Technology. His editorial roles and international visiting positions indicate extensive collaboration and grant-related activities, though specific grants are not detailed in the text. He leads the Distributed Systems Group at TU Wien, a research team focused on building next-generation distributed computing platforms, cloud services, and intelligent data processing systems with real-world applications in finance, enterprise systems, and large-scale data analytics.

Shlomo Havlinمشاهده پروفایل

استاد

• Statistical Physics
• Complex Networks
• Interdependent Systems
• +۷ مورد دیگر

Shlomo Havlin is a Professor in the Department of Physics at Bar-Ilan University, Israel, within the Faculty of Exact Sciences. He has held major academic leadership roles, including Dean of the Faculty of Exact Sciences (1999–2001), Chairman of the Physics Department (1984–1988), and President of the Israel Physical Society (1996–1999). He is also recognized as an External Faculty member at the Complexity Science Hub (CSH) in Vienna. His research focuses on statistical physics and its applications in complex networks , with far-reaching implications across disciplines such as climate science, biology, medicine, geophysics, and computer science. Havlin's work explores the behavior of interdependent systems, phase transitions, and systemic risk, contributing to foundational understanding in network resilience and abrupt system failures. The recent publications highlight a consistent trend in studying abrupt transitions and mixed-order phase transitions in complex systems, particularly focusing on the microscopic origins of such phenomena. These works sit at the intersection of statistical mechanics, network theory, and multidisciplinary modeling, often published in high-impact journals and preprint repositories. Scientific Awards: Landau Prize for outstanding Research (1988) Humboldt Senior Scientist Award (1992) Nicholson Medal, American Physical Society (2006) Weizmann Prize (2009) Lilienfeld Prize, American Physical Society (2010) Rothschild Prize in Physical and Chemical Sciences (2014) Order of the Star of Italy (2017) Israel Prize in Physics and Chemistry (2018) Bakhuis Roozeboom Medal Shlomo Havlin has led major research initiatives, including the Excellence National Network Center (Israel Science Foundation, 1990–2011) and the Minerva Center (1994–2011). He has been a Fellow of the American Physical Society since 1995 and the Institute of Physics (UK) since 2000. His editorial roles include membership on the boards of Physica A , New Journal of Physics , Fractals , and Co-Editor of Europhysics Letters . While specific grant details are not listed, his long-term leadership in nationally and internationally funded centers indicates sustained grant support and collaborative research mentorship. He is actively involved in the global network science community, as evidenced by participation in events like NetSci 2023 and organizing workshops such as the Statistical Mechanical Approaches of Complex Systems at CSH Vienna. His affiliation with the Complexity Science Hub underscores his engagement in interdisciplinary, large-scale scientific collaboration.

• Soil Microbiology
• Antibiotic Resistance
• Environmental Contaminants
• +۵ مورد دیگر

Kristian Koefoed Brandt serves as Associate Professor in the Department of Plant and Environmental Sciences at the University of Copenhagen's Faculty of Science, specializing in Microbial Ecology and Biotechnology. His research focuses on environmental microbiology, antibiotic resistance dynamics, and contaminant fate in agricultural systems. His primary research interests span Soil Microbiology, Antibiotic Resistance, Environmental Contaminants, and Wastewater Treatment, with significant contributions to understanding microbial processes in organic agriculture and wastewater systems. Key themes include bioremediation strategies, pesticide risk assessment, and the One Health implications of emerging contaminants. Analysis of his recent publications reveals a strong emphasis on antimicrobial resistance surveillance across environmental interfaces, particularly in wastewater-river systems and agricultural settings. His work integrates molecular techniques with large-scale environmental monitoring, often through European collaborative projects examining climate gradients and farming practices. 295 citations for 'Emerging contaminants: A One Health perspective' (2024) Featured in 13 news outlets and referenced in Wikipedia High-impact research on wastewater-based disease surveillance Extensive collaboration across European research networks Professor Brandt actively contributes to environmental risk assessment frameworks, particularly regarding recycled nutrients in organic agriculture. His research group employs advanced molecular markers and isotope techniques to study microbial interactions, with significant implications for sustainable agricultural practices and public health monitoring systems.

Patrick Munkمشاهده پروفایل

دانشیار

• Antimicrobial Resistance
• Genomic Epidemiology
• Metagenomics
• +۱۲ مورد دیگر

Patrick Munk is an Associate Professor at the National Food Institute, Technical University of Denmark (DTU), where he leads research in the Research Group for Genomic Epidemiology and contributes to the DTU Microbes Initiative. His work integrates genomics, metagenomics, and bioinformatics to study antimicrobial resistance (AMR) across human, animal, and environmental systems. He is actively involved in global surveillance efforts and is currently accepting PhD students. PhD, MSc Member, The Danish Young Academy (2024–2029) Patrick Munk’s research focuses on antimicrobial resistance, genomic epidemiology, metagenomics, and bioinformatics . His work leverages large-scale sequencing of sewage, livestock, and environmental samples to understand the global spread of resistance genes. He has led and contributed to major international studies, including the global sewage surveillance project across 101 countries, and has developed key genomic resources such as the PanRes and MetalResistance databases. His recent publications span high-impact journals like Nature Communications and Nature Microbiology , with themes centered on sewage-based surveillance, fecal resistomes in livestock, source attribution of microbial communities, and novel sequencing methodologies . His research has been widely covered in media and policy documents, reflecting its societal impact. Genomic analysis of sewage from 101 countries (Nature Communications, 2022) Time-series metagenomics for source-attributed surveillance (Nature Communications, 2024) High-throughput single-cell metagenomic sequencing (Frontiers in Microbiology, 2025) Patrick Munk has received recognition through significant media coverage, policy citations, and widespread academic engagement. His work has been highlighted in over 40 news outlets, referenced in policy sources and patents, and discussed across social media and academic platforms like Mendeley and Wikipedia. He supervises multiple PhD students and is a Principal Investigator or Supervisor on several active and completed projects, including Bioinformatic methods for multi-metagenome integration and Host range and epidemiology of antibiotic resistance . He is also a co-creator of key datasets that support open science in AMR research. His lab is part of a broad collaborative network involving DTU, international consortia, and public health agencies.

• Mixed Methods
• Digital Methods
• Social Media Research
• +۹ مورد دیگر

Hjalmar Alexander Bang Carlsen is an Associate Professor at the Copenhagen Center for Social Data Science (SODAS), Faculty of Social Sciences, University of Copenhagen. He specializes in mixed digital methods and is deeply engaged in research and teaching related to digital data analysis, particularly in the context of political and civic participation on social media. He is a key contributor to the Social Data Science master's degree program. University: University of Copenhagen School: Faculty of Social Sciences Department: Copenhagen Center for Social Data Science (SODAS) Position: Associate Professor in Mixed Digital Methods Email: hc@soc.ku.dk ORCID: https://orcid.org/0000-0002-2638-0932 His research centers on mixed methods strategies for digital data, with a substantive focus on civic and political engagement via social media. Key areas include informal volunteering during crises, gender inequality in online political participation, and the use of large language models (LLMs) for qualitative interviewing. He leads three major projects: SoMeVolunteer (on crisis volunteering), public participation on Facebook, and AInterviewer (an LLM-based interviewing tool). The recent publications reflect a strong trend in digital sociology, crisis response, and methodological innovation. His work combines large-scale social media data with surveys, interviews, and textual analysis, emphasizing ethical and epistemological considerations in computational social science. Topics span from refugee solidarity and pandemic volunteering to framing contests among climate NGOs and gender disparities in digital political engagement. While no formal scientific awards are listed in the provided text, Carlsen is actively funded by the Velux Foundation and UCPH Data+, and his work is widely disseminated through media and academic outlets. He collaborates closely with researchers like Jonas Toubøl and Snorre Ralund, and his projects often involve interdisciplinary teams. He has secured seed funding for innovative methodological development, indicating strong grant-writing capacity. He is involved in public engagement, with multiple media appearances discussing Danish civic response during the pandemic and refugee crises. His research outputs include journal articles, book chapters, and a co-authored textbook on mixed methods. He also participates in workshops and public lectures, contributing to both academic and public discourse on digital society. Carlsen is affiliated with SODAS and the Social Sciences Datalab, indicating active involvement in data-intensive research infrastructure. His work on AInterviewer suggests leadership in emerging AI-driven qualitative methods, positioning him at the forefront of digital social research innovation.

Maria Del Rio-Chanonaمشاهده پروفایل

استادیار

• Network Science
• Agent-Based Modeling
• Large Language Models
• +۷ مورد دیگر

Maria Del Rio-Chanona is an Assistant Professor in the Department of Computer Science at University College London (UCL), where she conducts interdisciplinary research at the intersection of network science, machine learning, and economic modeling. She is also a member of the External Faculty at the Complexity Science Hub since January 2025, reflecting her ongoing engagement with complex systems research. Her academic background includes a PhD in Mathematics from the University of Oxford, where she was part of the Institute for New Economic Thinking at the Oxford Martin School, and undergraduate studies in Physics at UNAM, Mexico. Her research interests center on understanding socioeconomic transformations using computational methods. She specializes in applying Large Language Models (LLMs), Agent-Based Models (ABMs), and network analysis to study the impacts of generative AI, the net-zero transition, and global crises like the COVID-19 pandemic on labor markets, economic resilience, and public discourse. Her work often involves analyzing large-scale digital trace data, including online labor platforms and social media, to uncover behavioral and structural shifts. The recent publications reveal a consistent focus on labor market dynamics in the face of technological disruption. Her work examines how generative AI reshapes demand for freelance skills, reduces public knowledge sharing on platforms like Stack Overflow, and influences mental health discourse in relation to employment decisions. She also investigates broader economic modeling, including pandemic shock propagation and employment transitions during decarbonization. Among her scientific recognitions is the Emerging Scientific Award from the Complex Systems Society in 2023. She has collaborated with international policy organizations such as the International Monetary Fund (IMF) and the International Labour Organisation (ILO), underscoring the policy relevance of her research. Maria Del Rio-Chanona has held prestigious research positions, including as a James S. McDonnell Foundation (JSMF) Postdoctoral Fellow at the Complexity Science Hub and a Visiting Fellow at the Growth Lab, Harvard Kennedy School. These roles have enabled her to lead and contribute to high-impact interdisciplinary projects. While no current advisees are listed, she actively engages with PhD opportunities and interdisciplinary research networks. She is involved in multiple research initiatives, including the development of HuBERT, an NLP algorithm for extending the Seshat Global History database, and projects studying labor transitions in net-zero scenarios. Her work on the HiST-LLM benchmark for historical knowledge in LLMs highlights her innovative integration of AI with social science.

• Bioinformatics
• Machine Learning
• Computational Biology
• +۷ مورد دیگر

Jan Baumbach is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he leads cutting-edge research at the intersection of computer science, bioinformatics, and biomedical data science. His work integrates machine learning, network biology, and systems medicine to address complex challenges in health and disease. His research interests include Bioinformatics , Machine Learning , Gene Regulatory Networks , Drug Repositioning , Biomedical AI , and Computational Biology . He applies advanced computational methods to analyze large-scale biological data, with applications in cancer, metabolic diseases, dermatology, and infectious diseases. The 15 most recent publications highlight a strong trend toward biomedical artificial intelligence , network-based analysis , and translational bioinformatics . His work spans from foundational machine learning in healthcare to clinical applications in osteoarthritis, bone regeneration, and skin biology. A consistent theme is the integration of multi-omics data and the development of privacy-preserving federated learning frameworks for distributed healthcare systems. Jan Baumbach has been principal investigator on several major research projects, including: EU Horizon2020: Privacy-preserving federated machine learning in distributed healthcare Danish National Research Foundation: ATLAS Center for Functional Genomics of Tissue Plasticity Villum Foundation: Big Data Bioinformatics (Young Investigator Programme) Carlsberg Foundation: Computational profiling of bacterial volatile metabolomes (ProVol) He has supervised 13 PhD students and has an extensive publication record of 262 works. His research has been featured in high-profile media outlets, emphasizing the societal impact of drug repositioning and AI in medicine. He leads a research group focused on computational systems biology , with strong collaborations across Europe in the fields of genomics, metabolomics, and clinical data science.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +۳ مورد دیگر

Ingemar Johansson Cox serves as a Professor within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His research bridges theoretical machine learning foundations with practical applications across medical data analysis, information retrieval, remote sensing, and sustainability initiatives. His research portfolio emphasizes machine learning applications in high-impact domains, particularly medical data analysis (e.g., early detection of gynecological malignancy using online search activity) and sustainability (e.g., reducing AI's carbon footprint). The Machine Learning section actively contributes to the university's SCIENCE AI Centre, focusing on both algorithmic innovation and real-world problem-solving in biological modeling and environmental monitoring. Recent publication trends reveal expanding work in quantum computing applications for biomolecular modeling, sustainable AI frameworks, and cross-cultural NLP systems. His 2024-2025 output demonstrates strong interdisciplinary collaboration, especially in medical informatics and climate-related AI research. Professor Cox operates within the Department of Computer Science's robust research ecosystem, which includes dedicated compute clusters and specialized initiatives like TreeSense for global tree resource monitoring through remote sensing and deep learning. The department's infrastructure supports large-scale machine learning projects requiring significant computational resources.

Alvaro Torralbaمشاهده پروفایل

دانشیار

• Symbolic Search
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Alvaro Torralba is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with the Technical Faculty of IT and Design. His research focuses on symbolic search, heuristic functions, and planning algorithms within artificial intelligence and machine learning. Notable projects include the ConAn initiative exploring contrastive analysis for state-space exploration. He has contributed extensively to classical planning, probabilistic planning, and automated planning competitions, earning awards such as the First Prize in the Agile Track of the 10th International Planning Competition (IPC’23). His work often bridges theoretical advancements with practical applications, including game-based network update synthesis and believable non-player character development. Research outputs include over 60 publications since 2011, with a focus on optimizing search algorithms and enhancing planning efficiency through techniques like operator-potential heuristics and bidirectional search strategies. His scientific contributions span algorithmic innovation, verification methodologies, and large-scale abstraction evaluation. Collaborations and datasets include foundational work on PDDL generators and pattern databases, with open-access resources available via Zenodo. As a program committee member and award-winning researcher, Torralba actively contributes to advancing the frontiers of AI planning and decision-making systems.

Gaël Le Mensمشاهده پروفایل

استاد

• Judgment Under Uncertainty
• Learning Processes
• Social Dynamics
• +۳ مورد دیگر

Gaël Le Mens is Professor (Catedràtic) in the Department of Economics and Business at Universitat Pompeu Fabra, with affiliations at Barcelona School of Economics and UPF-BSM. An ICREA Acadèmia Awardee (2023-2027), he researches learning processes in individuals, organizations, and machines, examining how information sampling affects judgment and belief formation. Current research explores: Social media feedback dynamics and opinion polarization Conceptual categorization using LLMs (GPT-4, Llama 3) Reinforcement learning biases Wisdom-of-crowds phenomena His work combines computational modeling with experimental methods. Publications appear in top journals including Psychological Review (4 papers), PNAS (4 papers), and Management Science. Research themes show consistent focus on decision biases arising from sampling limitations, with recent expansion into AI-assisted text analysis and political scaling applications. Honors include: ERC Consolidator Grant for selective information sampling research Multiple publications in PNAS and Psychological Review ICREA Acadèmia recognition Mentored doctoral students include Elizaveta Konovalova, Nikolas Schöll, and Thomas Woiczyk. Leads the ERC-funded project 'Implications of Selective Information Sampling' examining belief polarization mechanisms. Affiliated with the Rebel Governance Network and Strategic Organization Design Unit, with visiting positions at INSEAD, London Business School, and Stanford GSB.

Thomas Asmus Pedersenمشاهده پروفایل

دانشیار

• Circular Economy
• Sustainable Construction
• Building Materials
• +۵ مورد دیگر

Thomas Asmus Pedersen is an Associate Professor and Vice Head of Section at the Department of Technology and Innovation, Faculty of Civil and Architectural Engineering, University of Southern Denmark (SDU). His work bridges engineering and sustainability, with a strong focus on circular economy principles in the construction sector. His research interests lie at the intersection of sustainable construction, circular economy, and building material reuse. He investigates how second-hand building materials can be integrated into modern construction practices to reduce waste and mitigate climate change. His work often engages with policy, organizational structures, and entrepreneurial initiatives in Europe and the Western world. The most recent publication highlights a critical examination of platforms for second-hand building materials, analyzing their potential as hubs in the circular economy versus being speculative ventures by entrepreneurs. The research reflects interdisciplinary engagement across engineering, environmental science, and socio-economic dimensions. Member, Danmarks Akkrediteringsinstitution (2014–present) He is actively involved in teaching and supervision, including student practicum (B-PTE6) and final project guidance (Afgangsprojektvejledning). He also contributes to EU-level research through participation in the EUROFusion BB RH facility project (2024–2026). His collaborative network spans national and international institutions, with a focus on sustainable construction and resource efficiency. He works closely with colleagues on research, supervision, and large-scale technical projects.