جستجوی استاد راهنمای دانشگاهی

• Quantum Chemistry
• Computational Physics
• Materials Science
• +۳ مورد دیگر

Andreas Juul Bay-Smidt is a PhD fellow in the Department of Chemistry at the University of Copenhagen's Faculty of Science, based at Universitetsparken 5, 2100 Copenhagen Ø. His research focuses on computational approaches to quantum systems and molecular thermal dynamics. His primary research interests span Quantum Chemistry , Computational Physics , and Materials Science , with specialized work in quantum simulation algorithms and phonon transport engineering. Recent publications demonstrate expertise in fault-tolerant quantum computing frameworks and molecular-scale thermal management solutions using heavy-atom strategies. Analysis of his 2025 publications reveals a strong trend toward interdisciplinary computational methods , bridging quantum information science with condensed matter physics and materials design. Key thematic areas include error-resistant quantum simulations and nano-engineered thermal control mechanisms. No scientific awards or student advisement activities are documented in the available materials. Research appears conducted within collaborative frameworks, evidenced by multi-institutional authorship on recent publications, though specific lab affiliations are not stated.

• 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.

• Quantum Information
• Quantum Computing
• Quantum Technology

Thue Christian Thann Nikolajsen is a Research Fellow at the Niels Bohr Institute, University of Copenhagen, specializing in Quantum Information Science & Technology. His work focuses on advancing quantum computational frameworks and experimental implementations within the institute's dedicated research environment. His primary research domains include: Quantum Information theory and protocols Quantum Computing hardware and algorithms Quantum Communication systems Based at the Niels Bohr Institute's facilities at Blegdamsvej 19, 2100 Copenhagen, Denmark, he contributes to the Quantum Information Science & Technology research group. His institutional email thue.nikolajsen@nbi.ku.dk serves as the primary contact point for academic collaboration and inquiries regarding his quantum research initiatives.

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

Theo Juncker von Buchwald serves as a Guest Researcher in the Department of Chemistry at the University of Copenhagen, located at Universitetsparken 5 in Copenhagen Ø. His research focuses on advanced computational methods in quantum chemistry, particularly developing and implementing cluster perturbation theories for molecular excitation energies and ground-state properties. His primary research interests center on Computational and Theoretical Chemistry, specializing in electronic structure theory. He develops high-accuracy quantum chemical methods including coupled cluster theory, variational excitation-energy functions, and core-valence separation techniques. His work bridges theoretical chemistry with high-performance computing, implementing parallel algorithms for large-scale molecular systems. Analysis of his four recent publications reveals a consistent research trajectory in Cluster Perturbation Theory (CPT), with increasing computational sophistication. His work demonstrates expertise in both theoretical development (variational formulations, Lagrangian approaches) and practical implementation (parallelization, GPU acceleration). Key trends include targeting CCSD/CCSDT accuracy with reduced computational cost, developing diagnostics for model reliability, and extending CPT to core-excited states for X-ray spectroscopy applications. Research Collaborations and Infrastructure: Collaborates extensively with Kurt V. Mikkelsen, Poul Jørgensen, and Jeppe Olsen Utilizes Frontier supercomputer at Oak Ridge National Laboratory Employs hybrid MPI/OpenMP parallelization with GPU acceleration Receives funding from Danish Council for Independent Research (Grants DFF-0136-00081B, DFF-10.46540/3103-00261B)

• Quantum Chemistry
• Computational Chemistry
• Quantum Computing
• +۳ مورد دیگر

Gustav Stausbøll Hedemark serves as an Instructor and PhD fellow in the Department of Chemistry at the University of Copenhagen. His research spans critical interdisciplinary domains: Quantum Chemistry Computational Chemistry Quantum Computing Theoretical Chemistry Molecular Physics Spectroscopy His 2025 publication in the Journal of Chemical Theory and Computation pioneers quantum computational methods for hyperfine coupling constants, addressing hardware limitations and error propagation in near-term quantum devices. This work exemplifies the accelerating convergence of quantum information science with molecular property prediction, targeting spectroscopic applications where classical methods face exponential scaling challenges. As an Instructor, he delivers chemistry instruction while advancing his doctoral research through collaborative projects with computational chemistry groups at the university.

• Computational Chemistry
• Molecular Modeling
• Quantum Chemistry
• +۴ مورد دیگر

Jan Halborg Jensen is a Professor in the Department of Chemistry at the University of Copenhagen, with a distinguished research career in computational chemistry and molecular modeling. His work spans quantum chemistry, cheminformatics, and machine learning applications in chemical research, with significant contributions to drug design, catalyst discovery, and chemical reaction prediction. His primary research interests focus on Computational Chemistry , Molecular Modeling , and Quantum Chemistry , with particular expertise in applying machine learning techniques to chemical problems. Jensen's research integrates computational methods with practical chemical applications, including retrosynthesis planning, catalyst design, and drug discovery. His work bridges theoretical chemistry with practical applications in organic synthesis and pharmaceutical development. Analysis of Jensen's recent publications (2023-2025) reveals a strong focus on machine learning applications in chemistry, particularly for predicting chemical reactivity, designing catalysts, and advancing drug discovery. His work demonstrates increasing integration of artificial intelligence with traditional computational chemistry methods, with significant contributions to cheminformatics, transition metal chemistry, and biomolecular simulation standards. The research spans theoretical developments and practical applications across organic chemistry, biochemistry, and materials science. Jensen maintains an active research program with extensive publication output, including 88 research contributions comprising journal articles, book chapters, and books. His work shows strong collaborative networks across computational chemistry and related fields. He has presented on educational technology topics, including a lecture titled "Make a difference - teach and learn with technology" in April 2018, demonstrating engagement with pedagogical developments alongside research activities. His online presence includes a professional blog (molecularmodelingbasics.blogspot.com) and comprehensive CV documentation.

• Computational Chemistry
• Cheminformatics
• Quantum Chemistry
• +۵ مورد دیگر

Maria Harris Rasmussen is a Postdoctoral Researcher in the Department of Chemistry at the University of Copenhagen, where she conducts cutting-edge research at the intersection of computational chemistry, cheminformatics, and artificial intelligence. Her work focuses on developing and applying computational methodologies to solve complex chemical problems, with particular emphasis on reaction discovery, molecular representation, and catalyst design. Her research interests span multiple domains of computational chemistry, with primary focus on Cheminformatics where she develops algorithms for molecular representation including SMILES notation for transition metal complexes. In Quantum Chemistry , she investigates photoinduced electron transfer processes and reaction mechanisms using advanced simulation techniques. Her work in Machine Learning for Chemistry includes developing explainable AI methods for molecular property prediction and uncertainty quantification in chemical data sets. She also contributes significantly to Catalysis Research through computational approaches for de novo catalyst discovery and reaction screening. Analysis of her publication record reveals strong trends in developing computational tools that bridge theoretical chemistry with practical applications. Her recent work shows increasing integration of machine learning with traditional quantum chemical methods, particularly in the areas of reaction space exploration and catalyst discovery. The interdisciplinary nature of her research connects chemistry with computer science, physics, and data science, reflecting the evolving landscape of modern computational chemistry. Maria maintains active research collaborations with prominent scientists including Jensen J.H., Mikkelsen K.V., and several international researchers as evidenced by her publication record. Her computational methodologies have gained attention across academic and research communities, with multiple publications receiving significant readership on platforms like Mendeley and social media engagement.

• Computational Chemistry
• Catalysis
• Quantum Chemistry
• +۳ مورد دیگر

Julius Johannes Seumer is a Postdoctoral Research Fellow in the Department of Chemistry at the Faculty of Science, University of Copenhagen. His research focuses on computational approaches to catalyst design and chemical synthesis optimization, with expertise in quantum mechanics simulations and evolutionary algorithms. His primary research interests include computational chemistry, catalysis, quantum chemical modeling, and machine learning applications in chemical synthesis. Key areas involve regioselectivity prediction, transition metal complex analysis, nitrogen fixation catalysts, and automated synthesis planning using genetic algorithms. Analysis of his recent publications reveals strong trends in computational catalyst discovery, particularly using genetic algorithms for exploring chemical space beyond traditional libraries. His work spans organic, inorganic, and physical chemistry domains with emphasis on Morita-Baylis-Hillman reactions, C-H activation, and dinitrogen fixation. Seumer collaborates extensively with Professor Jens H. Jensen and other researchers at the University of Copenhagen, with publications appearing in high-impact journals including Angewandte Chemie , Chemical Science , and Beilstein Journal of Organic Chemistry . His computational methodologies have garnered attention across academic networks, with citations in Scopus and engagement on platforms including X (Twitter), Bluesky, and Mendeley. His work demonstrates significant interdisciplinary impact across chemistry and computational sciences.