جستجوی استاد راهنمای دانشگاهی

• Differential Privacy
• Algorithms
• Theoretical Computer Science
• +۳ مورد دیگر

Teresa Anna Steiner serves as an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, specializing in algorithmic research with emphasis on privacy-preserving computational methods and theoretical computer science. Her research centers on differential privacy mechanisms, where she investigates trade-offs between data utility and privacy guarantees through rigorous analysis of noise injection techniques like Laplace and Gaussian distributions. She extends this work to dynamic graph databases requiring real-time privacy protections and develops novel text indexing approaches for regular expression pattern matching, contributing to foundational advancements in algorithm design for sensitive data environments. Recent 2025 publications reveal a cohesive research trajectory focused on practical implementations of differential privacy across diverse data structures, with particular attention to variance optimization in noise mechanisms, edge-level privacy in evolving graphs, and efficient indexing for textual pattern recognition. These works collectively address critical challenges in balancing computational efficiency with robust privacy guarantees in modern data systems. No scientific awards were documented in the available information. Details regarding student advising or research grant funding were not specified in the provided materials.

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.

Henrik Jeldtoft Jensenمشاهده پروفایل

استاد

• Complexity Science
• Statistical Mechanics
• Mathematical Physics
• +۷ مورد دیگر

Henrik Jeldtoft Jensen is a Professor of Mathematical Physics and leads the Centre for Complexity Science at Imperial College London. His work spans multiple disciplines, focusing on the statistical mechanics of complex systems, with applications in physics, biology, neuroscience, and finance. Professor, Mathematical Physics Leader, Centre for Complexity Science Institution: Imperial College London His research interests lie at the intersection of theoretical physics and complex systems. He is best known for developing the Tangled Nature Model of evolving ecosystems, which has been extended into financial modeling through the Tangled Finance approach. His work in brain dynamics involves analyzing fMRI and EEG data using tools from statistical physics. He has made significant contributions to self-organized criticality and stochastic dynamics of complex systems, particularly in condensed matter and evolutionary contexts. The recent publications reflect a strong trend toward interdisciplinary complexity science, integrating concepts from physics, biology, economics, and neuroscience. Keywords across these works include complexity, statistical mechanics, dynamical systems, and network theory, with subfields ranging from neural avalanches to financial instability and biodiversity modeling. Henrik Jensen is the author of two influential books: Self-Organized Criticality and Stochastic Dynamics of Complex Systems (with Paolo Sibani), which have been widely cited across disciplines. He has supervised numerous PhD and postdoctoral researchers through the Centre for Complexity Science, though specific names are not listed. His research has been supported by grants from UK research councils and international collaborations, particularly in interdisciplinary complexity projects. He is affiliated with the Centre for Complexity Science, a multidisciplinary research hub at Imperial College London that brings together physicists, mathematicians, biologists, and social scientists to study complex adaptive systems.

Christian Kühnمشاهده پروفایل

استاد

• Dynamical Systems
• Differential Equations
• Stochastic Dynamics
• +۵ مورد دیگر

Christian Kühn is a Professor of Multiscale and Stochastic Dynamics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He has been an External Faculty member at the Complexity Science Hub Vienna since 2017, reflecting his interdisciplinary engagement in complex systems research. His academic background includes a BSc in Mathematics from Jacobs University Bremen (2005), an M.A.St. from the University of Cambridge (2006), and a PhD in Applied Mathematics from Cornell University (2010). He held postdoctoral positions at the Max Planck Institute for the Physics of Complex Systems in Dresden and the Vienna University of Technology, where he also served as an APART-Fellow and Leibniz Fellow. Christian Kühn's research lies at the intersection of differential equations, dynamical systems, and mathematical modeling. He focuses on multiscale problems, the impact of noise and uncertainty in deterministic and stochastic systems, and adaptive networks. Central phenomena of interest include bifurcations, pattern formation, and scaling laws. His work bridges theoretical developments with applications in epidemiology, neuroscience, and complex network dynamics. His recent publications (2021–2024) reflect a strong trend in analyzing nonlinear and stochastic dynamics on networks, with applications ranging from epidemic modeling to synchronization and critical transitions. Key themes include explosive phenomena, adaptive network behavior, moment closure methods, and non-Markovian systems, demonstrating a consistent focus on foundational aspects of dynamical systems with practical relevance. Notable scientific awards include: Richard-von-Mises Prize, GAMM (2017) Lichtenberg Professorship, VolkswagenStiftung (2016) Best Paper Award, TU Vienna (2014) Leibniz Fellow, Oberwolfach (2013) APART-Fellow, Austrian Academy of Sciences (2012) While specific details about advised students are not provided, his role as a full professor and active researcher suggests involvement in mentoring graduate students and postdoctoral researchers. His work has been supported by prestigious grants such as the Lichtenberg Professorship. He leads research in multiscale and stochastic dynamics, contributing to both theoretical advances and interdisciplinary applications. Kühn is part of vibrant research environments at TUM and the Complexity Science Hub Vienna, collaborating with leading scientists in network science, applied mathematics, and complex systems. His work continues to advance the understanding of critical transitions and nonlinear behavior in high-dimensional and stochastic systems.

Manfred Laubichlerمشاهده پروفایل

استاد

• Theoretical Biology
• Evolutionary Developmental Biology
• History of Biology
• +۷ مورد دیگر

Manfred Laubichler is President’s Professor of Theoretical Biology and History of Biology at Arizona State University (ASU), where he serves as Director of the School of Complex Adaptive Systems and the Global Biosocial Complexity Initiative. He is also an External Professor at the Santa Fe Institute (SFI) and co-director of the ASU-SFI Center for Biosocial Complex Systems. His academic affiliations include the Center for Social Dynamics and Complexity, the Center for Biology and Society, the Center for Evolution and Medicine, and the Water Institute at ASU. His educational background spans zoology, philosophy, and mathematics from the University of Vienna; biology from Yale University; and history/history of science from Princeton University. Laubichler's research centers on evolutionary novelties across scales—from genomes to knowledge systems—alongside the structure of evolutionary theory and the evolution of scientific knowledge. His work bridges theoretical biology, philosophy of science, and complex systems, with a strong emphasis on biosocial complexity and sustainability. He has contributed extensively to understanding the conceptual foundations of evolutionary developmental biology (Evo-Devo) and the historical and philosophical dimensions of biological theory. His recent publications reflect an interdisciplinary trend, integrating epidemiology, public health, and social behavior during crises such as the COVID-19 pandemic, while maintaining a core focus on theoretical biology, history of science, and complex systems. Themes include trust, scientific responsibility, epistemology of science, and modeling biosocial dynamics. Elected Fellow of the American Association for the Advancement of Science Fellow at the Wissenschaftskolleg zu Berlin Vice Chair of the Global Climate Forum Laubichler has led multiple NSF-funded research initiatives in digital humanities, history and philosophy of science, and complex adaptive systems. He has advised numerous doctoral projects and co-led training programs such as the Embryo Project. He is actively involved in interdisciplinary labs and centers, including the Information and Competition Lab, Complexity Economics Lab, and the Social Insect Research Group. His leadership extends to editorial roles in journals like Biological Theory , Journal of Experimental Zoology Part B , and the Archimedes series.

Athanasios Koliosمشاهده پروفایل

استاد

• Wind Turbine Engineering
• Offshore Wind Turbines Engineering
• Offshore Wind Farms Engineering
• +۳ مورد دیگر

Athanasios Kolios serves as Professor and Head of the Structural Integrity and Loads Assessment section within the Department of Wind and Energy Systems at the Technical University of Denmark (DTU). His work focuses on advancing wind energy technology through structural analysis, materials science, and system optimization for both onshore and offshore applications. His primary research domains include wind turbine structural integrity, offshore wind farm layout optimization, and structural health monitoring systems. He investigates critical challenges in operation and maintenance strategies, techno-economic metrics for wind projects, and materials behavior under dynamic loading conditions. His fingerprint analysis reveals dominant expertise in Wind Turbine Engineering (100%), Offshore Wind Turbines Engineering (45%), and Offshore Wind Farms Engineering (36%). Recent publications demonstrate strong emphasis on data-driven approaches for wind energy systems, including structural optimization algorithms, virtual sensing techniques, and techno-economic assessments of offshore projects. His work consistently bridges theoretical engineering principles with practical industry applications, particularly in Brazilian and European offshore wind contexts. Scientific Awards: No scientific awards were documented in the provided information Professor Kolios actively supervises five PhD candidates across major research initiatives while mentoring Master's students in structural wind energy applications. His research portfolio includes six significant projects addressing critical industry needs: PhD Supervision: Chopard (anomaly interpretation), Piovesan (risk-based technology qualification), Yildirim (floating turbine uncertainty), Rodrigues Faria (autonomous operation), Al-Hagri (offshore structure maintenance) Master's Supervision: Rushil S. Mahajan (monopile buckling analysis) Current Projects: Decision support systems, life cycle cost modeling, floating wind turbine modeling, autonomous operation frameworks, sustainable offshore structure design As section head at DTU Wind and Energy Systems, he leads an internationally collaborative research group focused on structural integrity assessment, load prediction methodologies, and materials innovation for next-generation wind turbines. The team maintains strong industry partnerships and contributes to global wind energy standards development through participation in initiatives like ISSC.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +۳ مورد دیگر

Freja Stær Hincheli serves as a Lecturer at the Department of Computer Science , University of Copenhagen. Her work intersects multiple domains within machine learning, with a particular emphasis on quantum-inspired algorithms, medical imaging, and sustainable AI development. Keywords : Machine Learning, Quantum Computing, Medical Imaging, Natural Language Processing, Computational Biology Key Collaborations : SCIENCE AI Centre Her research spans quantum-enhanced neural networks, explainable AI for medical diagnostics, and energy-aware model design. Recent publications highlight applications in cross-cultural recipe adaptation, emotion-aware dialogue systems, and climate-conscious AI strategies. The Machine Learning Section at DIKU focuses on theoretical foundations and applications including medical image analysis , biological data modeling , and quantum computing , aligning with her contributions.

Matthias Oliver Wilhelmمشاهده پروفایل

دانشیار

• Quantum Field Theory
• Mathematical Physics
• Special Functions
• +۳ مورد دیگر

Matthias Oliver Wilhelm is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, affiliated with the Quantum Mathematics research group. His work focuses on advanced theoretical physics topics including scattering amplitudes in gauge/gravity theories, Feynman integrals, special functions, and applications of machine learning in physics. He has contributed to groundbreaking research at the intersection of quantum field theory and mathematical physics, particularly in understanding gravitational wave phenomena and high-energy particle interactions. Research Interests: His research combines quantum field theory with algebraic geometry and computational methods, exploring topics like elliptic Feynman integrals, post-Minkowskian expansions, and machine learning-driven amplitude calculations. Recent work includes leveraging Calabi-Yau manifolds for gravity-related Feynman integrals and developing transformer-based algorithms for scattering amplitude computations. Awards: He received the Velux Grant - Villum Young Investigator in 2018, recognizing his innovative contributions to theoretical physics. Projects: Leveraging Algebraic Geometry for High-Precision Fundamental Physics (2024-2028, DFF-funded) Thermodynamics of strongly coupled Quantum Field Theory (2019-2027, private foundation-funded) Key Themes in Recent Work: His articles emphasize novel computational techniques (e.g., machine learning for integration-by-parts reduction), formal developments in scattering amplitude theory, and geometric approaches to quantum gravity problems. Notable contributions include classifying Feynman integral geometries for black-hole scattering and advancing elliptic function methodologies in perturbative QFT.

Chenjuan Guoمشاهده پروفایل

دانشیار

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +۴ مورد دیگر

Chenjuan Guo is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. She is affiliated with the Data Engineering, Science and Systems group and the AI for the People initiative, and is part of the Daisy - Center for Data-intensive Systems. Her research focuses on machine learning, data engineering, spatio-temporal data analysis, and time series forecasting. Key projects include the Villum Foundation-funded 'Explainable AI for Complex Microbial Community Interactions and Predictions' (2021-2024) and the Astra project on time series analytics in spatial networks (2018-2021). Her research interests span representation learning, autoencoders, path representation, outlier detection, trajectory data analysis, and time series modeling. She has supervised 3 PhD students and contributed to over 60 publications, with a recent emphasis on transformer-based forecasting, neural architecture search, and continuous learning frameworks for spatio-temporal data. Her work bridges theoretical advancements with practical applications in environmental science, cloud computing, and urban mobility systems. Key achievements include developing frameworks like AutoCTS++ for automated time series forecasting and LightGTS for lightweight models. She actively collaborates internationally, contributing to conferences like ECML PKDD and CVPR. Her research is supported by grants from the Villum Foundation and other institutions.

Elli Anastasiadiمشاهده پروفایل

استادیار

• Formal Verification
• Concurrency Theory
• Parallel Programs
• +۷ مورد دیگر

Elli Anastasiadi is an Assistant Professor in the Department of Computer Science at Aalborg University, part of the Technical Faculty of IT and Design. She is a member of the DEIS (Distributed, Embedded and Intelligent Systems) research group, which focuses on formal methods, verification, and intelligent systems. Prior to her current role, she was a postdoctoral researcher at Uppsala University and completed her PhD at Reykjavik University. Education: PhD in Computer Science, Reykjavik University (2022) Master’s in Applied Mathematics and Computer Science, NTUA, Greece Her research centers on formal verification of concurrent and parallel systems , with emphasis on runtime verification, process algebra, and logical foundations. She works extensively with hyperproperties, modal logic, and equational reasoning. Her work bridges theoretical computer science with practical verification tools. Recent publications show a strong trend in logic-based verification , particularly in modal and temporal logics, recursion, and monitor synthesis. Her work often involves complexity analysis and axiomatization of logical systems. Scientific Awards: PhD grant from Reykjavik University research fund She has advised no publicly listed students yet and is actively involved in academic service, including co-organizing workshops and being an invited speaker. She collaborates closely with leading researchers in concurrency theory and formal methods. Labs and Teams: Member of the DEIS research group at Aalborg University, contributing to projects on verification, distributed systems, and intelligent decision-making.

Jakob Kibsgaardمشاهده پروفایل

استاد

• Catalysis
• Electrocatalysis
• Ammonia Synthesis
• +۵ مورد دیگر

Jakob Kibsgaard is a Surface Physics and Catalysis Professor and Section Leader at the Department of Physics, Technical University of Denmark (DTU). He leads research in the VISION – Center for Visualizing Catalytic Processes and maintains an active research program focused on electrocatalysis, ammonia synthesis, and energy conversion technologies. His research interests span catalysis, electrocatalysis, ammonia synthesis, hydrogen production, CO2 conversion, and nanomaterials. Kibsgaard's work primarily addresses sustainable energy solutions through advanced catalyst design and characterization. His research group investigates fundamental processes at atomic and molecular levels to develop efficient catalysts for energy conversion reactions, with particular emphasis on electrochemical ammonia synthesis and CO2 hydrogenation. Analysis of his recent publications reveals a strong focus on dual and triple atom electrocatalysts, advanced imaging techniques for catalyst characterization, and innovative approaches to ammonia synthesis. His work spans multiple energy conversion reactions including CO2 reduction, nitrogen reduction, oxygen reduction, oxygen evolution, and hydrogen evolution. The research combines experimental approaches with theoretical insights to develop next-generation catalysts for sustainable energy applications. Kibsgaard actively supervises multiple PhD students including Thorlacius-Ussing, Tokman, Wu, Griffin, Kazaz, and Pedersen, with projects spanning controlled synthesis of catalysts, exploration of electrocatalysts at atomic scale, and discovery of new dual and triple atom electrocatalysts. His research is supported by various projects with both active and completed funding. His laboratory work centers around the Surface Physics and Catalysis section at DTU Physics, where he utilizes advanced characterization techniques including transmission electron microscopy, X-ray photoelectron spectroscopy, and custom-designed electrochemical cells to study catalytic processes at fundamental levels.

Anders Henry Nielsenمشاهده پروفایل

پژوهشگر

• Plasma Physics
• Fusion Energy
• Tokamak Devices
• +۷ مورد دیگر

Anders Henry Nielsen is a Senior Scientist in the Department of Physics at the Technical University of Denmark (DTU), specializing in Plasma Physics and Fusion Energy. He is based at DTU’s Fysikvej campus in Kgs. Lyngby, Denmark, and maintains an active research profile with over 350 publications. His work is central to advancing understanding in magnetic confinement fusion, particularly through computational modeling and experimental collaboration with major tokamak facilities worldwide. His research interests lie at the intersection of plasma turbulence, edge physics, and fusion energy. He investigates phenomena such as zonal flows, coherent structures, and transport scaling in tokamak plasmas. His work often involves developing and applying advanced numerical models, including coupling Monte Carlo methods with 2D fluid models like HESEL, to simulate neutral particle behavior and turbulence in the plasma edge. He has contributed to major experimental campaigns on devices such as TCV, ASDEX Upgrade, and EAST, focusing on heating, fueling, and stability. His recent publications highlight trends in computational plasma physics, parametric instabilities, and cross-field transport. These works span disciplines including plasma turbulence, magnetic confinement, and fusion reactor engineering, with subfields like Monte Carlo simulations, electron cyclotron resonance heating, and synthetic diagnostics. His research consistently addresses key challenges for ITER and DEMO, such as power threshold scaling and heat flux management. Anders Henry Nielsen has supervised multiple PhD students, including R. Gerru Miguelañez, G. Avdeeva, J. M. B. Olsen, and J. Madsen, on projects related to zonal flow dynamics, neutral injection, and turbulence modeling. He has received research funding from various sources, including national and international fusion programs, and has been involved in projects funded by research councils and institutional grants. He is affiliated with the Plasma Physics and Fusion Energy section at DTU, where he collaborates closely with leading researchers such as V. Naulin, J. J. Rasmussen, and S. Kragh Nielsen. His team contributes to both theoretical and experimental aspects of fusion science, participating in international collaborations and presenting findings at major conferences. He has organized academic events, such as the Ninth Sino-Danish Autumn School on Fusion Plasma Physics and Technology.

Thomas Martini Jørgensenمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Big Data
• +۳ مورد دیگر

Thomas Martini Jørgensen is a Senior Researcher at the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His work primarily focuses on machine learning applications in telecommunication networks and industrial processes. Key Research Areas: Machine Learning, Deep Learning, Network Impairments, Fault Detection Collaborations: Active collaborations in AI for cable broadband networks and optical network diagnostics Recent research involves integrating operations research with deep learning for network topology reconstruction and applying conditional diffusion models for fault detection in optical networks. His work spans both theoretical and applied domains, with a focus on industrial applications. He supervises PhD projects including: Anomaly Detection in Cable Broadband Networks Machine Learning Applications in Field Service His publications demonstrate expertise in convolutional neural networks for flocculation analysis and geomechanical modeling of fracture networks in petroleum engineering.

Katja Hoseمشاهده پروفایل

استاد

• Query Processing
• Semantic Web
• Linked Data
• +۷ مورد دیگر

Katja Hose is a Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. Her research focuses on Data, Knowledge and Web Engineering with specializations in AI for the People and Artificial Intelligence and Machine Learning. She maintains an active research profile with numerous publications and projects. Department of Computer Science Technical Faculty of IT and Design Aalborg University Research areas: Query Processing, Semantic Web, Linked Data, Knowledge Graphs Professor Hose's research interests center on knowledge representation, semantic web technologies, and AI applications. Her work spans from theoretical database systems to practical applications in healthcare, environmental assessment, and microbial data analysis. She has made significant contributions to knowledge graphs, large language models, and semantic search technologies, with particular emphasis on addressing hallucinations in AI systems and improving table search in semantic data lakes. Her recent publications demonstrate a strong trend toward integrating knowledge graphs with large language models, developing evaluation frameworks for AI hallucinations, and applying data science to diverse domains including healthcare and environmental sustainability. Her research bridges theoretical computer science with practical applications that address real-world challenges. NLP4KGC Best Paper Award (2023) ESWC 2023 Best Demo Award (2023) 2020 AMiner AI 2000 Most Influential Scholars AIME 2020 Best Paper Nomination (2020) ESWC 2019 Best Demo Award Nomination (2019) Professor Hose leads multiple significant research projects including ARISTOTLE (AI for clinical risk assessment), DarkScience (microbial data analysis), and the Poul Due Jensen Professorate in Big Data and AI. She has supervised numerous PhD students and collaborates extensively across disciplines, particularly in healthcare applications of AI and environmental assessment technologies. Her research has attracted substantial funding from sources like Villum Fonden and Danish E-infrastructure Cooperation. She is actively involved in several interdisciplinary research teams, including collaborations with microbiologists on microbial dark matter projects and with environmental scientists on digital environmental assessment systems. Her work on the ARISTOTLE project demonstrates strong connections between AI research and clinical applications, while her DarkScience project bridges computer science with microbiology.

• Astrophysics
• Cosmology
• Dark matter + dark energy
• +۷ مورد دیگر

Jens Hjorth is a Professor of Astrophysics at the University of Copenhagen's Niels Bohr Institute, where he leads research in the DARK center. With over 400 refereed publications, more than 35,000 citations, and an h-index of 96, he is a prominent figure in modern astrophysics. His work spans cosmology, dark matter research, and high-redshift galaxy studies, with approximately 33 papers published in Nature or Science journals. Professor Hjorth's primary research focuses on astrophysical transients, very high-redshift galaxies, cosmology, and the origin of universality in dark-matter halos. His work bridges theoretical modeling with observational data, particularly through his involvement with the Euclid space mission. His research often explores the intersection of astrophysics with art and science, demonstrating a commitment to interdisciplinary approaches. His recent publications reveal a strong emphasis on dark matter halo structure, galaxy evolution across cosmic time, and the development of sophisticated simulations for cosmological studies. His publication record shows consistent high-impact contributions, with recent work heavily focused on the Euclid mission's instrumentation and data analysis. These publications span theoretical cosmology, observational techniques, and the development of advanced simulation methods for understanding large-scale structure formation. The research demonstrates both depth in specialized areas like dark matter physics and breadth across related astrophysical disciplines. Villum Investigator: Time in Astrophysics Member of the boards of the Carlsberg Foundation Member of the boards of the Tuborg Foundation Approximately 33 scientific papers in Nature or Science journals Most cited lead-author paper: J. Hjorth et al. Nature 423, 847–850 (2003) with ~1300 citations As a Villum Investigator, Professor Hjorth leads significant research initiatives focused on time-domain astrophysics. He also serves as Co-lead of the UCPH Forward career development program, demonstrating his commitment to academic leadership and mentorship. His extensive publication record and high citation count reflect substantial research impact across multiple funding cycles and collaborative projects. Professor Hjorth is deeply involved with the DARK research center at the Niels Bohr Institute, which focuses on cosmology, dark matter, and dark energy research. His work with the Euclid mission places him at the forefront of international space-based cosmological surveys. The research teams he participates in combine observational astronomers, theoretical physicists, and computational scientists to tackle fundamental questions about the universe's structure and evolution.