Find and connect with academic supervisors, professors, and scholars for your research journey.
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Research Fellow
Ian Walter Orzel is a PhD Fellow in the Machine Learning section at the Department of Computer Science, University of Copenhagen . He is affiliated with the SCIENCE AI Centre and contributes to interdisciplinary research bridging machine learning with quantum computing, healthcare diagnostics, and environmental sustainability.
Lecturer
Simon Krogh Anderson serves as a Lecturer at the Department of Computer Science (DIKU), Faculty of Science, University of Copenhagen. He is an active member of the Machine Learning section which focuses on theoretical foundations and applications across domains including natural language processing, medical image analysis, and biological data modeling. The department participates in the SCIENCE AI Centre and maintains powerful compute resources including the TreeSense platform for remote sensing. His research spans multiple cutting-edge areas in artificial intelligence with particular emphasis on machine learning, quantum computing applications, and algorithmic fairness. Key interests include sustainable AI development, reproducibility in recommender systems, and cross-cultural adaptation frameworks. His work often bridges theoretical computer science with practical applications in environmental monitoring, healthcare analytics, and quantum information processing. Recent publications demonstrate strong interdisciplinary connections across quantum computing, sustainable AI, and fairness metrics. Trends show increasing focus on environmentally conscious AI development, integration of quantum methods with classical machine learning, and ethical considerations in recommendation systems. His work frequently leverages Denmark's extensive health registries and environmental data resources. Anderson actively contributes to the department's research ecosystem through teaching and collaboration within the Machine Learning section. While specific grants aren't detailed in available materials, his publications indicate involvement in projects related to quantum computing infrastructure, environmental monitoring systems, and AI ethics frameworks. The department provides significant computational resources including a dedicated cluster and specialized labs like TreeSense for remote sensing applications. His work appears connected to the SCIENCE AI Centre's initiatives in sustainable computing and quantum information processing.
Lecturer
Dongyu Gao serves as an Instructor in the Machine Learning section at the Department of Computer Science (DIKU), University of Copenhagen. His position places him within one of Scandinavia's leading computer science departments, which hosts the SCIENCE AI Centre and maintains strong connections with both theoretical and applied machine learning research. Dr. Gao's research interests center around machine learning with applications spanning information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. His work appears to bridge theoretical foundations with practical implementations, as evidenced by publications addressing quantum computing applications, environmentally sustainable AI practices, and advanced neural network architectures. The Machine Learning section at DIKU provides substantial computational resources including a powerful dedicated cluster and specialized initiatives like TreeSense for remote sensing applications. Analysis of recent publications associated with Dr. Gao reveals a diverse research portfolio spanning multiple cutting-edge AI domains. His work demonstrates particular strength in quantum machine learning applications, sustainable computing practices, and interpretable AI systems. The publications show a consistent pattern of interdisciplinary collaboration, connecting computer science with healthcare, environmental science, and quantum physics. Notably, several publications address the critical challenge of making AI systems more environmentally sustainable without sacrificing performance. The Machine Learning section operates within DIKU's broader research ecosystem, which includes strong connections to the SCIENCE AI Centre. This environment provides access to substantial computational resources and fosters collaboration across various AI subfields including natural language processing, computer vision, and theoretical machine learning. The department's location in Copenhagen positions it at the intersection of European AI research initiatives with strong connections to both academic and industry partners across the continent.
Lecturer
Marcus Friis Klausen is a Lecturer at the Department of Computer Science , University of Copenhagen, affiliated with the Machine Learning section. His work intersects theoretical and applied machine learning across interdisciplinary domains. Research interests include: Quantum machine learning and hardware acceleration Explainable AI and model interpretability Clinical and healthcare applications of NLP Neuroscience-informed language modeling Environmentally sustainable AI practices Geometric and non-Euclidean deep learning Recent publications show trends in: Quantum computing applications for molecular simulations Medical imaging and clinical decision support Algorithmic fairness and ethical information retrieval Neural network optimization for energy efficiency Biological data modeling and remote sensing
Lecturer
Lucas Alexander Kock is an Instructor at the Department of Computer Science , University of Copenhagen . His research spans Machine Learning and its applications in diverse domains including medical data analysis, quantum computing, and sustainable AI. Role: Lecturer in Machine Learning Affiliation: SCIENCE AI Centre, University of Copenhagen Research interests focus on: Quantum machine learning Neuroscience applications Cross-cultural AI systems Environmental sustainability in computing Medical informatics Deep learning explainability Recent publications demonstrate expertise in quantum computing applications , neural signal interpretation , and ethical AI frameworks . No formal awards or advisees are listed in available public data.
Lecturer
Jeppe Fræhr Linderød works as a Lecturer at the Department of Computer Science , University of Copenhagen. His research aligns with the department's Machine Learning section, focusing on theoretical foundations and applications in information retrieval, medical data analysis, remote sensing, and sustainability. He is part of the interdisciplinary SCIENCE AI Centre . His recent publications span diverse subfields including: Quantum machine learning and optical computing Explainable AI and feature attribution Large language models for emotion recognition Medical informatics applications Fairness in recommender systems Green/sustainable AI practices He contributes to the department's computational infrastructure, including access to a powerful compute cluster. His work often intersects with environmental and healthcare domains, particularly through projects like the TreeSense center for remote sensing applications.
Tobias Nordholm-Højskov is an Instructor at the Department of Computer Science , University of Copenhagen (DIKU). His research intersects machine learning with healthcare, sustainability, and quantum computing, focusing on theoretical foundations and applications in medical data analysis, climate-aware AI, and quantum systems. He is affiliated with the SCIENCE AI Centre and contributes to projects like QDarts (quantum dot array simulation) and TreeSense (remote sensing for environmental monitoring). His work spans diverse subfields, including Explainable AI for healthcare records Federated Learning in rare disease research Quantum-inspired neural networks Retrieval-Augmented Generation frameworks Environmental impact mitigation in AI
Thor Alexander Bøje Simonsen is an Instructor at the Department of Computer Science , University of Copenhagen , focusing on interdisciplinary research at the intersection of machine learning, quantum computing, and real-world applications. He is affiliated with the SCIENCE AI Centre and contributes to projects in sustainability, medical data analysis, and quantum-enhanced algorithms. His research spans theoretical and applied domains, including: Quantum computing for biomolecular simulations Environmentally sustainable AI systems Medical data analysis and clinical decision support Image reconstruction and remote sensing Explainable AI for large language models Thor's work engages with cutting-edge challenges in machine learning, from hardware acceleration to ethical considerations in clinical contexts. He is part of the university's Machine Learning Section , which has access to a dedicated compute cluster and collaborates with initiatives like TreeSense for global tree resource monitoring.
Associate Professor
Francesco Da Ros is an Associate Professor at the Technical University of Denmark , Department of Electrical and Photonics Engineering. His work focuses on machine learning applications in photonic systems, particularly in optical communication and photonic computing. Active in silicon photonics and nonlinear optics Key contributor to optical machine learning and digital signal processing Research Interests: Da Ros explores the intersection of machine learning and photonics , with specific projects involving: Photonic reservoir computing End-to-end optimization of optical communication systems Neuromorphic photonic circuits Channel equalization techniques Quantum communication systems Thermal crosstalk compensation in integrated photonics Scientific Contributions: His research has produced over 245 publications and 23 projects , including significant work on: Raman amplifier optimization Machine learning for optical matrix multipliers Frequency comb phase noise characterization Security systems using distributed acoustic sensing Awards: Recipient of prestigious awards such as: Best Young Italian Researcher in Denmark (2019) DOPS Prize (2017) Horizon Prize for Breaking Optical Transmission Barriers (2016) Academic Leadership: Actively supervises PhD students in projects related to: End-to-end learning for multi-core fiber systems Nonlinear fiber-optic channel optimization Quantum communication lasers Integrated quantum photonic reservoir computing
Associate Professor
Lars Pilgaard Mikkelsen is an Associate Professor in the Department of Wind and Energy Systems at the Technical University of Denmark (DTU). His research focuses on numerical finite element simulations and experimental characterization of polymer matrix composites, particularly in wind turbine blade materials. Key areas include fatigue and compression behavior, utilizing tools like Abaqus and 3D x-ray tomography. He has supervised numerous PhD and Master's projects, including topics such as pultruded carbon fiber composites and wind turbine blade redesign. Awards include the Simulia Champion (2020). Mikkelsen is involved in various committees and editorial roles, including the Wind Energy Science Journal and the Danish Center for Applied Mathematics and Mechanics. His work contributes to UN Sustainable Development Goals related to affordable and clean energy (SDG 7) and industry, innovation, and infrastructure (SDG 9). His research bridges computational modeling and experimental techniques to advance material science applications in renewable energy systems.
Researcher
Peter Kjær Willendrup serves as a Senior Research Engineer and Special Consultant at the Department of Physics at the Technical University of Denmark (DTU), where he has been employed since January 2012. He is also seconded to the European Spallation Source (ESS) Data Management and Software Center (DMSC), initially at 33% capacity from June 2014 to January 2023, and at 100% capacity since April 2023. His primary role involves leading major computational projects that support neutron and X-ray scattering research infrastructure. Willendrup's research spans multiple disciplines including neutron & X-ray scattering, computational physics, computer science, data analysis, project management, and software development. His work focuses on creating simulation tools for neutron scattering instrumentation, with particular emphasis on applications for the European Spallation Source in Lund, Sweden. He has made significant contributions to computational methods for neutron scattering experiments and instrument design. His publication record shows a strong trend toward neutron instrumentation development for the ESS facility, with increasing focus on simulation techniques, data analysis methods, and computational approaches to optimize neutron scattering experiments. Recent work demonstrates growing integration of machine learning techniques with traditional simulation methods, particularly for assisting users with model selection in neutron scattering experiments. Willendrup leads two major software projects: McStas (since 2002), which is world-leading in simulation of instrumentation for neutron scattering and virtual neutron scattering experiments, and McXtrace, its X-ray equivalent. These projects involve collaboration with multiple international institutions including DTU, ESS, NBI, ILL, and PSI. His educational background includes an M.Sc. in Physics and a B.Sc. in Mathematics from the University of Copenhagen (1992-2000). Prior to joining DTU, he worked as a research assistant at Rigshospitalet's Neurobiology Research Unit (2000-2002) and briefly as a physics teacher at Skt. Annæ Gymnasium (1998-1999). He is multilingual, speaking Danish, English, Swedish, German, and French fluently.
Professor
Ole Sigmund is a Professor at the Technical University of Denmark within the Department of Civil and Mechanical Engineering. He is affiliated with the NanoPhoton – Center for Nanophotonics and actively involved in research related to topology optimization, structural mechanics, and nanophotonics. Accepting PhD Students ORCID: 0000-0003-0344-7249 Research Interests: His work spans structural design, inverse methods, and multiphysics systems. Key areas include metamaterials, additive manufacturing, and photonic device optimization. Recent Publications (2021-2025): Focus on topology optimization for mechanical stability, nanophotonics, and multi-physics applications. Projects: Over 80 projects, including vibroacoustic systems, nanocavity design, and thermo-mechanical regulators. Collaborates with researchers like J.P. Groen and F. Wang.
Associate Professor
Thomas Christensen serves as Associate Professor in the Department of Electrical and Photonics Engineering at Technical University of Denmark (DTU), where he leads research within the Quantum and Laser Photonics group and NanoPhoton – Center for Nanophotonics. His work spans theoretical and experimental nanophotonics with emphasis on quantum effects in optical materials. His research fingerprint reveals deep expertise in graphene plasmonics (100%), photonic crystals (54%), and surface plasmons (46%), with significant contributions to two-dimensional materials (39%) and nanosphere optics (38%). Current investigations focus on quantum surface responses, nanoscale 3D printing of photonic structures, and adaptive optical systems using 2D materials. Recent publications demonstrate a clear trajectory toward quantum-classical hybrid systems, with 2024-2025 works exploring Feibelman d-parameters, multimodal AI for materials discovery, and visible-spectrum photonic crystal fabrication. His collaborative network spans 25 publications showing strong international engagement in nanophotonics. As main supervisor for Wang, M.'s ongoing PhD project 'Symmetry and topology in photonic systems' (2023-2026), he maintains active mentorship. His previous doctoral work 'Graphene Plasmonics' (2012-2016) established foundational contributions to plasmonic multipole theory and hydrodynamic modeling. Christensen operates within DTU's NanoPhoton infrastructure, leveraging advanced nanofabrication capabilities for quantum optics experiments. His research integrates theoretical modeling with experimental validation through collaborations highlighted by significant social media attention (31 X users, 2 news outlets) for key publications.
Associate Professor
Anderson de Souza Castelo Oliveira is an Associate Professor at Aalborg University's Department of Materials and Production within The Faculty of Engineering and Science. He holds a PhD in Biomedical Engineering (2012) and specializes in biomechanics, gait analysis, and sensor technologies. His research focuses on applications such as neurological disorders (e.g., Parkinson's disease), sports biomechanics, and exoskeletons for older adults. Oliveira has supervised one PhD student and contributed to over 116 publications since 2010. His recent work includes studies on mandibular movement classification, EEG activity in elite athletes, and reliable gait analysis using inertial measurement units (IMUs). Education: PhD in Biomedical Engineering (2012) Research interests span biomechanical analysis of human movement, sensor reliability, and neurophysiological mechanisms underlying motor control. His articles often address technical challenges in wearable sensor data interpretation and clinical applications. Notable contributions include improving gait event detection algorithms and assessing exoskeleton efficacy in aging populations.
Researcher
Emil Alstrup Jensen is a PhD student at the Department of Applied Mathematics and Computer Science , Technical University of Denmark. His research focuses on integrating Raman spectroscopy , machine learning , and microfluidic platforms for biomedical applications, including blood typing and flow cytometry innovations. Current projects include Raman spectroscopy and Multi-Modal Machine Learning Models (2024–2027), supervised by L. K. H. Clemmensen, A. Kristensen, and L. E. Pedersen. Collaborations span microfluidics , biomedical engineering , and applied data analysis . His publications highlight advancements in high-throughput Raman spectroscopy , label-free blood analysis , and viscoelastic fluid-based cytometry , often utilizing optical fiber coupling and particle suspension dynamics to improve diagnostic accuracy and efficiency. Project details: Raman spectroscopy and Multi-Modal Machine Learning Models (Active PhD project, 2024–2027) Supervisors: Lars Kai Hansen (Main Supervisor) Anders Kristensen Lars Erik Pedersen Research trends: Combining Raman spectroscopy with artificial intelligence for clinical diagnostics Designing microfluidic platforms for capillary flow cytometry using viscoelastic fluids Optimizing non-transparent solution analysis via flow cell engineering Contact: ealje@dtu.dk
