Find Academic Supervisors

Mads Røge EldrupView profile

Assistant Professor

• Coastal Engineering
• Fluid Dynamics
• Numerical Modeling
• +4 more

Mads Røge Eldrup is an Assistant Professor at the Department of the Built Environment, Aalborg University, within the Faculty of Engineering and Science. His research focuses on coastal engineering, wave dynamics, and structural resilience of marine infrastructure. He leads the Ocean and Coastal Engineering Research Group and contributes to the BLUE – Marine & Maritime Research initiative. Research interests include breakwater stability, nonlinear wave modeling, physical model testing, and numerical simulation techniques. Key projects involve the RESCUER initiative (2024-2028) addressing resilient coastal solutions. He has authored/co-authored 46 publications since 2014, with recent work on NL-SORS wave decomposition, submerged bar dynamics, and rock armour stability. He actively participates in international conferences and workshops, including sessions on Smoothed Particle Hydrodynamics and nonlinear wavemaker theory. Collaborations span institutions globally, focusing on coastal resilience and hydraulic engineering challenges.

Per LynggaardView profile

Professor

• Integrated Circuit Design
• Wireless Sensor Networks (WSN)
• Machine Learning
• +8 more

Per Lynggaard is a Professor of Electronics at the Technical University of Denmark (DTU) , leading the B.Eng. program in Electronics. Previously, he held an Associate Professor role at Aalborg University, combining academic excellence with a robust industrial career in technical-scientific research and development. Education: M.Sc. in Electrical Engineering and Information Technology (EE and IT) Ph.D. in Electronics from Aalborg University Research Interests: Focus on Integrated Circuit Design, Wireless Sensor Networks (WSN), Machine Learning, IoT, and Smart City Technologies . His work emphasizes energy-efficient systems, cybersecurity in IoT, AI-driven interference mitigation, and sustainable energy harvesting solutions. He has contributed to UN Sustainable Development Goals through projects addressing smart infrastructure and environmental monitoring. Projects & Collaborations: Leads and participates in EU-funded initiatives such as InnoTech (2023–2025) for green transition solutions and TransportTech (2023–2026) for Industry 4.0 logistics. Active in cybersecurity research via projects like Jamming Against Critical Wireless Communication , aiming to protect critical infrastructure. Awards: Recognized with multiple honors and rewards during his industrial career, though specific names are not listed. His work has been cited widely, with notable impact in IoT security and energy-efficient systems. Advising & Grants: Supervises Turnip T.N. in a PhD project on 6G security protocols. Engaged in securing funding for projects like F2D2: The Community for Dynamic Data (2021–2030), focusing on dynamic data systems and cybersecurity. Labs & Teams: Collaborates in interdisciplinary teams such as the InnoTech TaskForce and F2D2 Community , advancing IoT and AI integration. His research bridges academia and industry, with outputs spanning smart cities, healthcare IoT, and sustainable energy systems.

• Metal Forming
• Joining Processes
• Tribology
• +5 more

Chris Valentin Nielsen is an Associate Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU). His research focuses on metal forming, joining processes, and tribology, with expertise in formability, tool development, and numerical modeling. His work contributes to UN Sustainable Development Goals related to sustainable manufacturing. He supervises PhD students in projects such as sustainable busbars for electric vehicles and adjustable tool design for high-volume production. His research interests include metal forming (e.g., deep drawing, ironing), joining technologies (resistance welding, laser welding), and advanced manufacturing methods like additive manufacturing. He employs finite element modeling and experimental analysis to bridge fundamental and applied research. Collaborations span global institutions, addressing challenges in material behavior, process optimization, and tool durability. Recent publications explore topics such as dieless Nakajima testing for additive materials, punch design improvements, and asperity deformation mechanics. His work emphasizes sustainability, robust production systems, and eco-friendly lubrication solutions. Projects involve interdisciplinary teams, integrating numerical simulations with industrial applications to enhance manufacturing efficiency and material performance.

Andreas Bjerre-NielsenView profile

Associate Professor

• Educational Public Policy
• AI for Public Policy
• Social Networks
• +6 more

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.

• Gravitational Physics
• Astrophysics
• High-Energy Physics
• +3 more

Juno Chan is a Research Fellow at the Niels Bohr Institute , University of Copenhagen , specializing in Theoretical High Energy, Astroparticle and Gravitational Physics . Holding a permanent position since 2025, Chan contributes to advanced research in gravitational wave astronomy and neutron star dynamics. Role: PhD Fellow (converted to Research Fellow) Location: Blegdamsvej 17, Copenhagen Ø Contact: chun.lung.chan@nbi.ku.dk , +45 35 32 87 21 Chan's research focuses on gravitational wave detection , neutron star magnetohydrodynamics , and gravitational lensing in astrophysical contexts. Key contributions include: Developing gravitational wave detection algorithms in lensed systems Modeling magnetized rotating neutron stars with relativistic MHD simulations Curating the LensCAT catalog for known gravitational lenses Multi-messenger lensing studies through LIGO-Virgo-KAGRA collaborations Publications and Research Trends Chan's 15 most recent publications (2023-2025) demonstrate expertise in gravitational wave astronomy , neutron star physics , and computational astrophysics . The work involves: Waveform analysis for lensed events MHD simulations of compact objects Multi-messenger lensing frameworks Signal processing for detector networks Cosmological implications of lensing Collaborations span international institutions in gravitational wave networks, with frequent contributions to Physical Review D and Monthly Notices of the Royal Astronomical Society . No formal awards or student mentorship details are publicly available.

• Image Registration
• Image Segmentation
• Image Classification
• +6 more

Sune Darkner is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in the Image Analysis, Computational Modelling, and Geometry research section. His work focuses on medical image processing with particular emphasis on neuro-imaging data including MRI and PET scans. His primary research interests include Image Registration, Segmentation and Classification of Medical Image Data , with a specific focus on estimation of image similarity as his main research interest. Darkner strongly believes that the implementation of image processing algorithms should be thoroughly tested and reflect the theoretical properties as accurately as possible. His work primarily centers on neuro-imaging data such as MRI and PET. His recent publications (2024-2025) reveal a strong focus on medical image analysis, with particular emphasis on tumor volume delineation, deformable image registration with physics constraints, and applications of deep learning in medical imaging. His work spans both theoretical foundations of image processing and practical clinical applications. Darkner previously held a Post Doc position at the Technical University of Denmark from February 2009 to January 2010, demonstrating his longstanding engagement with image analysis research in the Danish academic community.

Krist V. GernaeyView profile

Professor

• Industrial fermentation
• Bioprocess modeling
• Process Analytical Technology
• +2 more

Krist V. Gernaey is Professor in Industrial Fermentation Technology at the Technical University of Denmark's Department of Chemical and Biochemical Engineering. His research develops computational tools for bioprocess optimization across pharmaceutical, food, and chemical sectors. Research specializes in mechanistic modeling of fermentation processes, process analytical technology (PAT) implementation, and continuous production system design. Current investigations focus on uncertainty analysis methods, data-driven modeling, and novel bioreactor characterization from micro to production scale. Publications demonstrate applications in vaccine manufacturing, wastewater treatment, chromatography simulation, and sustainable chemical engineering. Recent work advances regulatory frameworks for in silico bioprocess models and AI integration in engineering education. Research collaborations span academic institutions and industry partners across Europe. Professional activities include conference organization and editorial responsibilities for chemical engineering journals.

Mikkel Lønborg FriisView profile

Associate Clinical Professor

• Surgical Simulation
• Medical Education
• Ultrasound Training
• +7 more

Mikkel Lønborg Friis is a Clinical Associate Professor at Aalborg University, affiliated with the Department of Clinical Medicine under The Faculty of Medicine. He also serves as a senior consultant ( Ledende overlæge ) at Aalborg University Hospital, where he leads simulation-based training initiatives at NordSim – Centre for Skills Training and Simulation. His dual academic and clinical roles position him at the intersection of advanced surgical practice and innovative medical education. Clinical Associate Professor, Aalborg University Ledende overlæge (Chief Physician), Aalborg University Hospital Member, NordSim – Centre for Skills Training and Simulation His research interests focus on enhancing surgical and diagnostic competencies through technology-driven education. Key areas include simulation-based training, artificial intelligence in fetal and surgical ultrasound, robotic surgery assessment using deep learning, and curriculum development for cross-specialty ultrasound education. He actively contributes to improving clinical outcomes in pilonidal sinus disease and advancing AI integration in medical imaging. The recent publications highlight a strong trend toward interdisciplinary innovation, particularly in blending AI, simulation, and medical education. His work spans clinical surgery, educational methodology, and computational analysis of surgical performance. A significant portion of his research involves designing and evaluating training protocols, developing datasets for skill assessment, and exploring ethical and practical implications of AI in clinical settings. Mikkel Friis has not been publicly recognized with scientific awards in the provided text, but his leadership in simulation and curriculum design suggests significant institutional impact. He is involved in mentoring and advising through collaborative research projects, particularly in simulation and ultrasound education. While formal students are not listed, his role in study protocols and dataset creation implies supervision of junior researchers and medical trainees. He participates in funded or institutionally supported research activities related to medical education innovation and surgical technology. His involvement in press and media coverage further underscores his role as a thought leader in clinical simulation careers. Friis is deeply embedded in NordSim – Centre for Skills Training and Simulation, where he contributes to developing and implementing simulation-based assessment tools, particularly for abdominal ultrasound and surgical skills. His team collaborates across departments and institutions, focusing on creating standardized, scalable training models that integrate emerging technologies like AI and virtual reality.

Jeppe Revall FrisvadView profile

Associate Professor

• Computer Graphics
• Digital Image Synthesis
• Realistic Rendering
• +7 more

Jeppe Revall Frisvad is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), within the Visual Computing group. His work bridges computer graphics, material science, and applied optics, focusing on realistic rendering and material appearance modeling. Ph.D. in Computer Graphics, DTU Informatics (2008) M.Sc. in Engineering (Applied Mathematics), DTU (2004) Education in University Teaching, LearningLab DTU (2008–2009) His research centers on computing material appearance from physical and chemical properties, developing faster and more accurate physically based rendering methods. Key areas include light scattering, bidirectional reflectance distribution functions (BRDF), anisotropy, and translucency modeling. Applications span computer games, movies, digital prototyping, architectural visualization, and training simulators. His recent publications (2024–2025) reflect a strong trend in digitizing material appearance, especially for 3D printing and food science, using spectrophotometry and optical validation. The work combines computer graphics with interdisciplinary applications in food structure and biological imaging, emphasizing measurement, modeling, and simulation. Scientific Awards: Research travel prize from AEG Elektronfonden (2006) IGDA scholarship for GDCE 2005 DTU Ph.D. scholarship (2004) He actively supervises Ph.D. students and leads multiple research projects, including those on organ-on-chip imaging, cheese quality analysis, and optical modeling of teeth. He collaborates across disciplines and institutions, with external research stays at UC San Diego and the University of Otago. He is involved in projects integrating AI with 3D imaging and material digitization. Jeppe is a key member of the Visual Computing research environment at DTU, contributing to both fundamental rendering algorithms and practical applications in industry and science.

Gregor GiebelView profile

Researcher

• Wind Power Forecasting
• Renewable Energy Integration
• Wind Farm Flow Control
• +7 more

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.

Mauricio BustamanteView profile

Assistant Professor

• Astroparticle physics
• Neutrino physics
• Ultra-high-energy astrophysical neutrinos and cosmic rays
• +5 more

Mauricio Bustamante is an Assistant Professor at the Niels Bohr Institute , University of Copenhagen, specializing in theoretical high-energy astrophysics, astroparticle physics, and neutrino phenomenology. His research bridges cosmic phenomena with fundamental particle physics, focusing on ultra-high-energy neutrinos, cosmic rays, gamma-ray bursts, and new physics beyond the Standard Model. PhD in Physics (2012-2014) M.Sc. in Physics (2007-2010) B.Sc. in Physics (2001-2006) His work explores neutrino oscillations, self-interactions, and decay in extreme astrophysical environments. He contributes to major international collaborations like GRAND (Giant Radio Array for Neutrino Detection) and IceCube-Gen2, developing simulation pipelines and forecasting detection methods for EeV-scale neutrinos. Recent publications highlight energy-dependent flavor transitions, Lorentz invariance testing, and constraints on long-range neutrino interactions via DUNE and T2HK experiments. He actively participates in peer review for journals such as Physical Review D , Physical Review Letters , and Astrophysical Journal , and has attended conferences like TeV Particle Astrophysics (2017). His research emphasizes detector design, cosmic ray reconstruction via graph neural networks, and multi-messenger astronomy.

• Systems Biology
• Stress Response
• Stem Cell Differentiation
• +3 more

Ala Trusina is an Associate Professor at the Niels Bohr Institute , University of Copenhagen , specializing in Biocomplexity and Biophysics . Her research integrates coarse-grained modeling to study complex biological systems. Key Research Areas Stress Response Systems (diabetes, aging, cancer, inflammation) Stem Cell Differentiation (cell fate coordination, reversibility of states) Complex Systems (species coexistence, epidemics, CRISPR-phage interactions) Methodologies Theoretical: Agent-based modeling, in-silico simulations Experimental: Quantitative single-cell imaging, RNA/protein profiling Collaborations Joshua Brickman (ES cells), Anne Grapin-Botton (pancreas), Feroz Papa (diabetes), Else Kai Hoffman (p53 dynamics) Thomas Mandrup-Poulsen (inflammation), Savas Tay (spatio-temporal regulation) Teaching Physics of Molecular Diseases Numerical Methods in Physics

François Raymond J CornetView profile

Researcher

• Organometallic Chemistry
• Computational Chemistry
• Catalyst Design
• +3 more

François Raymond J Cornet is a Postdoctoral Researcher in the Department of Energy Conversion and Storage and a PhD Student in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His dual affiliation bridges energy conversion research and computational science, focusing on AI-driven molecular design. His research spans Organometallic Chemistry , Computational Chemistry , and Machine Learning , with specialization in catalyst design through diffusion models and inverse design methodologies. Key areas include metallocene chemistry, density functional theory applications, and generative modeling for chemical space exploration, targeting organometallic complexes like Vaska's complex. Cornet's publication trajectory reveals a concentrated effort in advancing equivariant diffusion models for molecular generation, particularly addressing small-data challenges in catalyst design. His work consistently integrates quantum chemistry with deep generative architectures, establishing new paradigms for inverse-design pipelines in computational chemistry. No scientific awards were documented in the source material. He recently completed the PhD project Machine learning for electronic scale inverse design of enzymatic catalysts (2021-2025) under supervisors M. N. Schmidt (primary), A. Bhowmik, and O. Winther, with examiners W. K. Boomsma and S. Olsson. Collaborators include P. Deshmukh, B. Benediktsson, and C. A. Naesseth across multiple publications. Research operations occur within DTU's interdisciplinary framework connecting the Department of Energy Conversion and Storage and Department of Applied Mathematics and Computer Science, leveraging computational infrastructure for molecular simulations and AI model training.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +5 more

Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.

Peter Behrensdorff PoulsenView profile

Researcher

• Solar Cell Engineering
• Photovoltaics
• Drone Engineering
• +5 more

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.