جست‌وجوی استاد و پژوهشگر

Jacob Østergaardمشاهده پروفایل

استاد

• Renewable Energy Systems
• Power System Stability
• Flexibility Markets
• +۸ مورد دیگر

Jacob Østergaard is a Professor and Head of the Division for Power and Energy Systems at DTU Wind and Energy Systems, Technical University of Denmark. His research focuses on renewable energy systems, offshore wind power hubs, and quantum computing applications in energy systems. He leads initiatives like EnergyLab Nordhavn and PowerLabDK, emphasizing collaboration between academia and industry. Education: MSc in Electrical Engineering from DTU (1989–1995). External positions include roles at Research Institute of the Danish Electric Utilities and Ørsted (now SK Energy). Research Interests: Power system stability, flexibility markets, offshore wind energy, quantum computing in energy systems, Power-to-X, and energy storage. He advocates for integrated, market-based energy systems to achieve the green transition. Publications highlight quantum computing for grid optimization, offshore energy hubs, and Denmark’s energy island strategy. Recent work emphasizes scientific advice for energy policy and green hydrogen production. Awards: A. Angelo’s Prize (1996), AEG Electron Prize (2007), Danish Design Award (2019), and EU RESponsible Island Prize (2020). Advising and Grants: Supervises PhD students in grid integration and control. Active in projects like OEH (Offshore Energy Hubs) and BOSS (Battery Energy Storage System). His work drives Denmark’s energy policy through roles on Energinet’s board and the Danish Energy Commission. Labs/Teams: Leads PowerLabDK and EnergyLab Nordhavn, experimental facilities for smart grid and energy system research.

• Quantum Information
• Quantum Computation
• Quantum Noise Characterization
• +۴ مورد دیگر

Daniel Stilck França is an Associate Professor at the Department of Mathematical Sciences within the Faculty of Natural and Life Sciences at the University of Copenhagen . He is affiliated with the QMATH Centre for Quantum Mathematics and related research networks. His research focuses on quantum information and computation , particularly on noise characterization in quantum systems, its impact on computational tasks, and quantum-inspired convex optimization algorithms. Recent work explores tensor networks and quantum error mitigation limitations. Key publications (2023-2025) address topics like Pauli channel estimation, Hamiltonian parameter learning, and quantum simulator scalability. His work has been featured in Nature Communications , Nature Physics , and ACM/IEEE conferences.

Kohei Nakajimaمشاهده پروفایل

دانشیار

• Soft Robotics
• Physical Reservoir Computing
• Physical Deep Learning
• +۵ مورد دیگر

Kohei Nakajima is an Associate Professor at the Department of Intelligent Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo. He holds concurrent positions at the Department of Creative Informatics and the Next Generation Artificial Intelligence Research Center (AI Center). As an Endowed Chair in Advanced Artificial Intelligence Education, he leads the Physical Intelligence Lab, which focuses on the intersection of soft robotics, nonlinear dynamics, and physical computing. His research interests center on Physical Reservoir Computing (PRC), a paradigm that exploits the natural dynamics of physical systems for computation, with applications in soft robotics, spintronics, and quantum machine learning. Nakajima's work demonstrates how physical systems can inherently process information without traditional digital computation, leveraging phenomena like chaos, bifurcations, and embodied intelligence. Nakajima's publications reveal a strong focus on understanding how physical systems can perform computational tasks. His recent work spans from biological applications (jellyfish cyborgs, ostrich-inspired robotics) to fundamental theoretical advances in reservoir computing. The research demonstrates how physical phenomena can be harnessed for information processing, with implications for energy-efficient computing and novel robotic control paradigms. As the organizer of the Reservoir Computing Seminar, Nakajima has built a vibrant research community exploring the nature of information processing across disciplines. His lab actively recruits graduate students and postdocs, indicating strong research momentum and institutional support for his work in physical intelligence.

Andrea Crovettoمشاهده پروفایل

دانشیار

• Photovoltaics
• Solar Energy Conversion
• Thin Film Technology
• +۳ مورد دیگر

Andrea Crovetto is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the National Centre for Nano Fabrication and Characterization and the Department of Nanofabrication. His research focuses on advanced materials for photovoltaic applications, including solar cell technologies, thin films, and semiconductor materials. His work contributes to UN Sustainable Development Goals related to affordable and clean energy. Key research interests include photovoltaic materials discovery, tandem solar cell design, and semiconductor characterization. Notable achievements include developing monolithic selenium/silicon tandem solar cells and pioneering studies on phosphosulfide semiconductors. He has authored over 90 publications and datasets, including high-impact articles in journals like JPhys Energy and PRX Energy . Dr. Crovetto has received the Young Scientist Award (2016) and actively supervises PhD students in projects such as Experimental Discovery of Phosphosulfide Materials for Solar Cells and Thiophosphate Thin Films for Quantum Technology . His research also involves collaborations on material synthesis, computational modeling, and device fabrication. He has presented at international conferences, including talks on monolithic tandem solar cells and materials discovery methodologies. His lab, based at DTU’s Produktionstorvet , integrates experimental and theoretical approaches to advance sustainable energy technologies.

• Microgrid Control Systems
• Renewable Energy Integration
• Smart Grids
• +۳ مورد دیگر

Juan C. Vasquez is a Professor at Aalborg University's Faculty of Engineering and Science, Department of Energy Technology, and Co-Director of the Center for Research on Microgrids (CROM). He holds a PhD in Automatic Control from the Technical University of Catalonia and has held academic positions at Aalborg University since 2011. His research focuses on microgrid control, renewable energy integration, power electronics, and smart grids. He has supervised numerous PhD and master’s students and leads projects funded by EU and national grants. Education: BS in Electronics Engineering (Autonomous University of Manizales, Colombia, 2004); PhD in Automatic Control (Technical University of Catalonia, Spain, 2009). Research interests include operation and control strategies for AC/DC microgrids, maritime microgrids, energy management systems, and IoT integration in smart grids. He has authored 648+ publications, including highly cited works, and received awards like the Young Investigator Award (2019) and Clarivate’s Highly Cited Researcher status since 2017. Key projects: EU-DREAM (Digital Services for Energy Transition), NEST (National Research Infrastructure), and ActRes (Resilience in Energy Systems). Collaborations include Virginia Tech and Ritsumeikan University.

Jesper Nygårdمشاهده پروفایل

استاد

• Solid state physics
• Nanophysics
• Quantum technology
• +۷ مورد دیگر

Jesper Nygård is a Professor at the Niels Bohr Institute, University of Copenhagen, specializing in solid state physics, nanophysics, and quantum technology. He leads the Center for Quantum Devices and has held leadership roles including Head of Section for Nanophysics and Solid State Physics (2007–2017) and Deputy Head of Research (2017–present). His research focuses on hybrid superconductor-semiconductor systems, nanowire-based quantum devices, and low-temperature quantum transport. PhD in experimental nanophysics (2000) and MSc/BSc in physics/mathematics from the University of Copenhagen International research experience at Harvard, Berkeley, and CNRS Grenoble His work bridges nanofabrication, quantum electronics, and Kondo physics, with recent publications analyzing nanowire junctions, microwave dynamics in superconducting systems, and heat dissipation mechanisms. He co-founded multiple technology startups and served as a Danish astronaut candidate (2005–2008). Scientific Awards: Member of the Royal Danish Society of Letters Member of the Danish Academy of Sciences

Sadegh Talebiمشاهده پروفایل

استادیار

• Reinforcement Learning
• Markov Decision Processes
• Online Learning
• +۲ مورد دیگر

Sadegh Talebi is a Tenure Track Assistant Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen . His research focuses on theoretical aspects of reinforcement learning, Markov decision processes, online learning, stochastic multi-armed bandit problems, and resource allocation in networks. Education BSc in Electrical Engineering (minor: Electronics) from Iran University of Science and Technology (IUST) (2004) MSc in Electrical Engineering (minor: Communication Systems) from Sharif University of Technology (2006) PhD in Electrical Engineering from the Department of Automatic Control at KTH Royal Institute of Technology (supervised by Alexandre Proutiere and Mikael Johansson) Research Specializes in theoretical foundations of reinforcement learning and online learning Key contributions in stochastic optimization, MDPs, and bandit algorithms Collaborates on applications in resource allocation and quantum computing Publications include high-impact work on offline RL, differentially private exploration, and scalable MDP solutions in journals like Neural Processing Letters and conferences such as NeurIPS and UAI.

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +۷ مورد دیگر

Luka Radic is a Researcher in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His work bridges theoretical and applied research in machine learning, with a focus on quantum machine learning , large language models , and fairness in AI systems.

Diego F. Aranhaمشاهده پروفایل

دانشیار

• Cryptography
• Cybersecurity
• Homomorphic Encryption
• +۷ مورد دیگر

Diego F. Aranha is an Associate Professor in the Department of Computer Science at Aarhus University . His research focuses on cryptographic systems, cybersecurity, and privacy-preserving technologies with applications in voting systems, post-quantum cryptography, and secure computation. He has contributed extensively to homomorphic encryption, secure multiparty computation (MPC), and cryptanalysis of cryptographic implementations. Key projects include: MPCC (2025-2028) : Multi-Party Computation in the Confidential Cloud SCI (2024-2027) : Secure Computation Infrastructures for the Retail Industry RENAIS (2021-2026) : Residue Number Systems for Cryptography His work emphasizes practical efficiency and formal verification of cryptographic protocols. Recent publications highlight advancements in lattice-based cryptography, secure voting schemes, and mitigating side-channel vulnerabilities in post-quantum algorithms. He actively collaborates on open-source cryptographic libraries and standards, with a focus on bridging theoretical security and real-world implementation challenges.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۵ مورد دیگر

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.

Joerg Jinschekمشاهده پروفایل

استاد

• Materials Science
• Additive Manufacturing
• Electron Microscopy
• +۴ مورد دیگر

Joerg Jinschek is a Professor at the Technical University of Denmark (DTU), leading the Nano-Micro-Macro. Structure in Materials department. He is affiliated with the National Centre for Nano Fabrication and Characterization and the VISION – Center for Visualizing Catalytic Processes. His research focuses on materials characterization, additive manufacturing, electron microscopy, and nanomaterials. Jinschek supervises multiple PhD projects, including studies on quantum dot ligands, electrocatalysts, and additive manufactured metal components. He has over 70 publications in high-impact journals and holds editorial roles, such as a reviewer for the Journal of Materials Science . Key research interests include in-situ TEM/SEM analysis of phase transformations, radiation effects in materials, and structural characterization of additively manufactured alloys. His work contributes to Sustainable Development Goals related to clean energy and industrial innovation. Jinschek’s lab, nanolab.dtu.dk , emphasizes cutting-edge microscopy techniques and microstructural analysis for advanced materials. Education: Dr. rer. nat. (PhD) in Materials Science Affiliations: National Centre for Nano Fabrication, VISION Catalytic Processes Center Grants/Projects: 9 active projects focusing on additive manufacturing, catalysis, and material characterization His recent work highlights advancements in functional graded alloys, electron beam damage mitigation, and laser-induced nanomaterials for electrochemical sensing. Jinschek collaborates internationally and actively engages in conference presentations and peer-review activities.

• 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.

• Bioinformatics
• Protein Stability
• Molecular Biology
• +۳ مورد دیگر

Amelie Stein is an Associate Professor at the Department of Biology, University of Copenhagen, specializing in Bioinformatics and RNA Biology. Her research focuses on protein stability, molecular mechanisms of disease variants, and computational methods for protein design. She is affiliated with the UCPH Quantum Hub, reflecting interdisciplinary interests in biological systems. Her work integrates bioinformatics tools, mutational scanning, and structural biology to understand protein degradation pathways and their relevance to human diseases such as Lynch syndrome and metabolic disorders. Key research areas include analyzing protein variants using deep learning models (e.g., SSEmb), developing web-based tools like MutationExplorer for 3D visualization, and characterizing disease-linked mutations in proteins such as Parkin and MLH1. Her publications highlight breakthroughs in rapid protein stability predictions, degon mapping, and the interplay between protein toxicity and degradation. No scientific awards are explicitly mentioned in the provided texts. Stein’s research also explores the application of computational approaches to biotechnology and therapeutic development, emphasizing translational applications of her findings. Her lab, linked to the SCARB research group (https://www1.bio.ku.dk/english/research/scarb/), focuses on structural and computational biology, with ongoing projects involving protein quality control networks and enzyme variant analysis. Collaborations span molecular biology, bioinformatics, and interdisciplinary quantum-related research through her UCPH Quantum Hub membership.

Niels Aageمشاهده پروفایل

دانشیار

• Topology Optimization
• Acoustics and Vibroacoustics
• Biomechanics
• +۵ مورد دیگر

Niels Aage is an Associate Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU). His research focuses on topology optimization, biomechanics, and multiphysics modeling, with applications in acoustic devices, biomedical implants, and microelectromechanical systems (MEMS). He holds roles such as Vice President of the International Society for Structural and Multidisciplinary Optimization (2023–2027). Education and Professional Background: Conducted a 5-month research visit at the University of Colorado, Boulder (USA) in 2010. Specializes in giga-scale numerical modeling, finite element methods, and topology optimization algorithms. Research Interests: Develops novel methods for topology optimization of fluidic, thermal, and acoustic systems. Explores applications in patient-specific spinal implants, metamaterials with vibroacoustic bandgaps, and nonlinear dynamic substructuring. His work integrates machine learning and reduced-order modeling for efficient simulation. Publications: Over 100 peer-reviewed articles, including recent contributions on connectivity promotion in topology optimization (2025), vibroacoustic metamaterial design (2025), and anatomically conforming spinal fusion cages (2024). Research emphasizes high-resolution modeling and bridging computational design with additive manufacturing. Scientific Awards: ISSMO Haftka Young Investigator Award (2021), Equinor Prize 2020, and Hyperion Innovation Excellence Award (2017). Recognized for contributions to structural optimization and computational mechanics. Advising and Grants: Supervises multiple PhD projects, including work on vibroacoustic shape optimization, quantum-opto-mechanical systems, and smart hearing aid modeling. Engages in collaborative projects funded by industry and academia. Labs/Teams: Collaborates with DTU’s Solid Mechanics group and industry partners on projects involving topology optimization, multiphysics simulation, and biomedical engineering. Active in international conferences and serves on editorial boards.

Yiyu Ouمشاهده پروفایل

دانشیار

• Optoelectronics
• Light-Emitting Diodes (LEDs)
• Photonic Engineering
• +۷ مورد دیگر

Yiyu Ou is an Associate Professor in the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU), where she leads research in optoelectronic devices, particularly light-emitting diodes (LEDs) and biomedical photonics. Her work bridges materials science, semiconductor engineering, and sustainable technology development. Department: Electrical and Photonics Engineering Institution: Technical University of Denmark (DTU) Research Focus: LED Systems, Silicon Carbide, Biomedical Implants Her research interests center on the development of advanced optoelectronic materials and devices, with a strong emphasis on nitride-based LEDs, fluorescent silicon carbide, and wireless optoelectronic biomedical implants. She explores novel packaging techniques, light extraction efficiency, and UV disinfection systems, contributing significantly to sustainable photonics and healthcare technologies. The recent trend in her publications shows a clear trajectory toward applied photonics in medicine and environmental health, including wireless implants, UV-B phototherapy, and wearable disinfection systems. Her work combines semiconductor physics with biomedical applications, demonstrating innovation in both materials and system integration. Scientific Awards and Recognition: No specific awards listed in the provided text. Yiyu Ou has served as Principal Investigator (PI) and supervisor on multiple research projects, including the DISOLE disinfection initiative and PhD supervision. Her grants focus on implantable UV-LED systems and innovative white LED light sources, often funded by Danish and European research councils. She collaborates extensively across disciplines, particularly with medical researchers and material scientists. She is affiliated with the Photonics group at DTU Fotonik, where her team works on diode lasers and LED systems. Research areas include porous SiC fabrication, hybrid white LEDs, and high-efficiency nitride devices, contributing to both fundamental science and commercial applications.