جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Image Processing
• Evolution Strategies
• +۲ مورد دیگر

Oswin Krause is an Associate Professor at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning . His research focuses on applying machine learning techniques to diverse domains including quantum computing, medical imaging, and astrophysics. Quantum dot array optimization and Coulomb diamond estimation Medical image analysis for clinical applications Evolutionary optimization algorithms and deep learning Neural networks for astronomical data interpretation Recent publications demonstrate a strong emphasis on quantum device calibration (2025), medical outcome prediction (2024), and algorithmic improvements in optimization (2022-2023). While no specific scientific awards are mentioned in the data, his work appears in journals like Physical Review Applied and Medical Image Analysis . Collaborations extend across physics, medicine, and computer science disciplines.

• Machine Learning
• Natural Language Processing
• Medical Data Analysis
• +۳ مورد دیگر

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.

• Machine Learning
• Natural Language Processing
• Medical Data Analysis
• +۱۰ مورد دیگر

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

• Programming Languages
• Theory of Computation
• Formal Verification
• +۳ مورد دیگر

Atta Samiullah serves as an Instructor in the Department of Computer Science at the University of Copenhagen, affiliated with the Programming Languages and Theory of Computation (PLTC) section. His role encompasses teaching responsibilities across undergraduate and graduate computer science programs including BSc in Computer Science, Computer Science and Economics, Machine Learning and Data Science, and Communication & IT. His research profile aligns with PLTC's core focus areas: Programming language technology and formal verification methodologies Theory of computation with applications in distributed ledger systems Security and privacy preservation techniques in software systems Financial technology applications and blockchain infrastructure Compiler technology and high-performance computing architectures Quantum programming language design The PLTC section operates through specialized research groups including Decentralized Systems, Security & Privacy, HOT Lab, FUTHARK (Functional Technology for Modern Architectures), and Quantum Programming. These groups maintain active industry collaborations in fintech and blockchain sectors while developing open-source tools like Moscow ML and MLKit compilers. Research output spans probabilistic programming frameworks, Stein variational inference techniques, and applications in bioinformatics and vaccine design. No scientific awards or student supervision activities are documented in current institutional records. His work contributes to the department's mission in advancing programming language theory with practical implementations in secure financial systems and distributed computing environments.

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

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.

Francesco Da Rosمشاهده پروفایل

دانشیار

• Optical Computing
• Optical Machine Learning
• Nonlinear Optics
• +۶ مورد دیگر

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

Ole Sigmundمشاهده پروفایل

استاد

• Topology Optimization
• Structural Mechanics
• Nanophotonics
• +۴ مورد دیگر

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.

Thomas Christensenمشاهده پروفایل

دانشیار

• Graphene
• Plasmonics
• Photonic Crystal
• +۴ مورد دیگر

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.

• Computability Theory
• Rewriting
• Lambda Calculus
• +۳ مورد دیگر

Jakob Grue Simonsen is a Professor and Department Chair at the Department of Computer Science (DIKU), University of Copenhagen. He holds a Dr. Scient., PhD, and MBA. His research focuses on the mathematics of computation, including computability theory, term rewriting, lambda calculus, complexity hierarchies, and symbolic dynamics, with applications in information retrieval and human-computer interaction. His primary research explores infinite computational processes and their finite representations, alongside practical work in neural hashing, fact-checking systems, and quantum language models. Previously, he contributed to constructive mathematics and biocomputing. An analysis of his 15 most recent publications (2015–2022) reveals strong emphasis on: Theoretical computability and game-theoretic models Neural networks for NLP (BERT, semantic hashing) Fact verification and explainable AI methodologies Efficient retrieval algorithms and recommendation systems Awards: RTA 2004 Best Paper Award CHI '13 Honorable Mention CHI '14 Honorable Mention

Nahid Binandeh Dehaghaniمشاهده پروفایل

پژوهشگر ارشد

• Quantum Optimal Control
• Quantum Entanglement
• Physics-Informed Neural Networks
• +۳ مورد دیگر

Nahid Binandeh Dehaghani is a Postdoctoral Researcher at Aalborg University's Department of Electronic Systems within The Technical Faculty of IT and Design. Her work focuses on quantum control systems and optimal control theory for quantum applications. Her research interests span Quantum Optimal Control , Quantum Entanglement , and Physics-Informed Neural Networks , with significant contributions to Pontryagin's Maximum Principle applications in quantum systems. Key research areas include quantum state engineering, entanglement synthesis, and coherence preservation in multi-level quantum systems. Analysis of her recent publications (2024-2025) reveals a strong trend toward hybrid quantum-classical computing approaches, particularly using physics-informed neural networks for solving quantum optimal control problems. Her work bridges theoretical control theory with practical quantum computing implementations, focusing on entanglement optimization and state preservation. She actively collaborates with researchers including Aguiar and Wisniewski, contributing to advancements in quantum information processing and quantum control methodologies.

Ryutaro Yamashitaمشاهده پروفایل

استاد مدعو

• Quantum Computing
• Cryptography
• Error Correction
• +۳ مورد دیگر

Ryutaro Yamashita is an Adjunct Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science. His research spans quantum computing, cryptography, and machine learning. Focus areas include quantum error correction, secret sharing schemes, and adversarial attacks on depth estimation networks. Collaborates on entanglement-assisted codes and finite field applications. Recent work highlights trends in securing quantum information systems and enhancing neural network robustness.

Haiyuan Wangمشاهده پروفایل

پژوهشگر

• Two-Dimensional Materials
• Perovskite Solar Cells
• Boron Nitride
• +۷ مورد دیگر

Haiyuan Wang is a Researcher at the Department of Energy Conversion and Storage at the Technical University of Denmark (DTU) . Their work focuses on atomic-scale modeling of materials for energy applications, particularly perovskite solar cells and quantum emitters in 2D materials. Research Interests Research spans Perovskite solar cell stability and defect passivation Quantum emitter engineering in 2D materials Machine learning for materials discovery Strain engineering in oxide membranes Electronic and vibronic coupling at interfaces Recent Publications Key contributions include 2025: Defect passivation in perovskite photovoltaics 2025: Room-temperature quantum emitters in α-MoO₃ 2024: Spatial conformation engineering for stable perovskite cells with methodologies ranging from first-principles simulations to data-driven approaches.

• Quantum Chemistry
• Computational Chemistry
• Theoretical Chemistry
• +۲ مورد دیگر

Andreas Erbs Hillers-Bendtsen serves as a Researcher (Postdoc) in the Department of Chemistry at the University of Copenhagen, where he conducts advanced computational research in quantum chemical methodologies and materials design. His work bridges theoretical frameworks with practical applications in energy and atmospheric science. His research spans Quantum Chemistry, Computational Chemistry, and Materials Science, with specialized focus on cluster perturbation theory for excited states, isotopic fractionation dynamics, and molecular systems for solar energy storage. Recent work demonstrates expertise in developing scalable quantum chemistry algorithms and applying them to photochemical processes and novel carbon-based materials. Analysis of his 51 research outputs reveals a strong trajectory in methodological innovation, particularly in extending cluster perturbation theory frameworks and implementing parallel computing solutions. His publications show increasing interdisciplinary reach, connecting quantum chemistry with atmospheric science and renewable energy materials, evidenced by publications in high-impact journals including Science Advances and Journal of Chemical Physics . Dr. Hillers-Bendtsen maintains active collaborations with leading researchers including Kurt V. Mikkelsen, Mads B. Johansen, and Trygve Helgaker, with co-authorship spanning theoretical developments, experimental validation, and materials synthesis projects. His work has attracted attention across academic platforms including Mendeley and social media, indicating growing influence in computational chemistry circles.

• Quantum Computing
• Condensed Matter Physics
• Quantum Dot Devices
• +۳ مورد دیگر

Torbjørn Raasø Rasmussen is a Researcher at the Niels Bohr Institute, University of Copenhagen, specializing in Condensed Matter Physics with a focus on quantum computing hardware development. Based at Universitetsparken 5, Copenhagen, he can be contacted via phone (+4535323593) or email (torbjoern.rasmussen@nbi.ku.dk), and his research group operates under https://nbi.ku.dk/Forskning/faststoffysik/. His research centers on overcoming experimental barriers in quantum device engineering through: Autonomous quantum dot device calibration systems Real-time spin qubit control mechanisms Machine learning applications for quantum transport characterization Gate reflectometry techniques for dense quantum arrays Automated charge state transition detection High-dimensional data optimization for quantum hardware Analysis of his six publications (2022-2025) reveals a cohesive research trajectory toward fully automated quantum computing platforms, with significant contributions in Physical Review Applied , Nature Communications , and npj Quantum Information . His work demonstrates increasing technical sophistication in quantum control systems, particularly through interdisciplinary approaches combining condensed matter physics, nanoelectronics, and computational methods. Collaborations span multiple international institutions as evidenced by co-authorship networks across quantum research hubs.

• Computational Biology
• Structural Biology
• Molecular Dynamics
• +۴ مورد دیگر

Emil Thomasen serves as a Guest Researcher in the Department of Biology within the Biomolecular Sciences unit at the University of Copenhagen's Faculty of Science. His research integrates computational methods with experimental structural biology approaches to investigate complex biomolecular systems. Position: Guest Researcher Institution: University of Copenhagen Department: Department of Biology Research Unit: Biomolecular Sciences Contact: fe.thomasen@bio.ku.dk | +4535323586 Thomasen's research focuses on developing and applying computational methodologies to understand protein structure, dynamics, and interactions. His work bridges quantum mechanics, molecular dynamics simulations, and experimental techniques including small-angle X-ray scattering (SAXS) and cryo-electron microscopy. He has made significant contributions to coarse-grained modeling, particularly with the Martini force field, and has pioneered approaches combining machine learning with quantum mechanical calculations for large molecular assemblies. His publication record demonstrates a clear trajectory in computational structural biology, with recent work emphasizing the integration of multiple experimental and computational techniques to characterize complex protein systems. The research shows increasing sophistication in handling large-scale biomolecular assemblies and applying advanced statistical methods like Bayesian inference for model refinement. Thomasen has established strong collaborative networks, frequently working with Professor K. Lindorff-Larsen and other researchers across computational chemistry and structural biology disciplines. His work has appeared in high-impact journals including Nature Communications, eLife, and Journal of Chemical Theory and Computation. As a recently completed Ph.D. graduate (2024), Thomasen continues to advance his research program as a Guest Researcher, building on his doctoral work focused on integrating coarse-grained molecular dynamics with SAXS data for characterizing multidomain protein assemblies.