Professor Dorte Juul Jensen holds a position at the Technical University of Denmark (DTU), affiliated with the Manufacturing Engineering department within the Civil and Mechanical Engineering school. She specializes in advanced materials science, focusing on microstructure analysis, additive manufacturing, and metallurgical processes. Research Interests: Dr. Jensen's work centers on understanding microstructural evolution in metals, particularly through additive manufacturing techniques. Her studies investigate cellular structures in stainless steels, particle-stimulated nucleation in aluminum alloys, and grain boundary dynamics in recrystallization processes. She employs cutting-edge methods such as 3D X-ray tomography and deep learning for microstructural characterization. Key Contributions: Her recent work includes breakthroughs in additive manufacturing-induced microstructures, high-energy X-ray diffraction techniques, and the application of machine learning for grain detection. She has authored over 538 publications and leads projects exploring materials' mechanical properties and failure mechanisms in critical components like turbine blades and railway switches. Awards: While no specific awards are listed, her extensive publication record and leadership in high-impact studies reflect significant contributions to the field of materials science. Advising: Dr. Jensen supervises PhD students and collaborates on grants focused on materials characterization and manufacturing innovations.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۱۰۶-۱۲۰ از ۱۳۳ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Peter Stanley Jørgensen is a Senior Researcher in the Department of Energy Conversion and Storage at the Technical University of Denmark (DTU), specializing in advanced imaging techniques for energy materials characterization. His work spans multiple areas of materials science with a focus on energy applications. Dr. Jørgensen earned his M.Sc.(eng) from IMM at DTU between 1999 and 2006, followed by a research assistant position at the university from 2006 to 2007. His research interests include: 3D image analysis and acquisition techniques Scanning electron microscopy and X-ray tomography applications Microstructure analysis of energy conversion materials Solid Oxide Fuel Cells and related electrochemical systems Materials characterization for sustainable energy solutions Dr. Jørgensen's recent publications demonstrate a strong trend toward developing and applying advanced 3D imaging methodologies to understand materials at micro and nano scales, with particular emphasis on solid oxide cells, ceramic materials, and solar technology. His work combines experimental characterization with computational analysis to address challenges in energy materials. His scientific contributions support UN Sustainable Development Goals related to affordable and clean energy. Dr. Jørgensen actively supervises PhD students across multiple projects: N. M. Leal Reyes (Physics-informed Deep Learning for Energy Materials) Y. Shang (Phase field modeling of microstructure evolution) S. Pollok (Deep Learning for optimizing magnets) E. O. Brenne (Physical model priors for tomogram segmentation) His research is supported by numerous projects including the Marie Skłodowska-Curie actions and multiple DTU-funded initiatives focused on energy materials characterization and analysis.
Varvara Zaniaمشاهده پروفایل
دانشیار
Varvara Zania is an Associate Professor in the Department of Environmental and Resource Engineering at the Technical University of Denmark (DTU). Her research focuses on geotechnical engineering, soil-structure interaction, and seismic analysis, with contributions to sustainable infrastructure and soil improvement technologies. **Education**: PhD in Mechanics (2009), Technical University of Crete MSc in Underground Constructions (2004), National Technical University of Athens Civil Engineering Diploma (2003), National Technical University of Athens **Research Interests**: Dynamic soil-structure interaction Seismic stability of geotechnical systems Physical modeling and centrifuge testing Geosynthetics and soil improvement techniques Probabilistic geotechnical analysis **Recent Research Trends**: Her work emphasizes sustainable infrastructure solutions, probabilistic site characterization, and advanced numerical modeling. Key areas include deep excavations, offshore wind turbine foundations, and geotechnical responses under extreme loading. **Advising & Projects**: Supervises PhD students on topics like deep excavation behavior, rammed earth materials, and soil parameter uncertainty. Leads projects funded by DTU and industry partners, focusing on sustainable geotechnical design and infrastructure resilience. **Labs/Teams**: Engaged in multidisciplinary teams at DTU, collaborating on projects involving geotechnical testing, numerical simulations, and field applications.
Frieder Lucklum is a Professor and Head of the Centre for Acoustic-Mechanical Microsystems (CAMM) at the Technical University of Denmark (DTU), Department of Electrical and Photonics Engineering Acoustic Technology. He holds a Ph.D. in Mechatronics/Acoustic Sensors from Johannes Kepler University Linz (2010) and a M.Sc. in Microsystems Engineering from Otto-von-Guericke-University Magdeburg (2005). His research focuses on acoustic and elastic metamaterials, phononic crystals, electroacoustic transducers, and additive manufacturing of microsystems. He is a Senior Member of the IEEE and has contributed to numerous academic conferences and editorial activities. Research Interests: Elastic and acoustic metamaterials/phononic crystals Electroacoustic transducers and acoustic sensors Additive manufacturing of microsystems Vibroacoustic characterization and fluidic systems Recent Projects & Awards: Recipient of Emerging Leaders 2021 and IOP Outstanding Reviewer awards (2020–2021). Supervising multiple PhD projects, including Measurement Methods for Acoustic-Mechanical Microsystems and Bayesian Transfer Path Analysis for Hearing Aids . Labs & Collaborations: Leading the CAMM lab at DTU, focusing on advanced acoustic and microsystem technologies. Collaborations include work on phononic-fluidic sensors, metamaterials, and additive manufacturing solutions for biomedical and environmental applications.
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.
Rasmus Tangsgaard Varneskov is a Professor of Statistics and Financial Econometrics at the Department of Finance, Copenhagen Business School (CBS). He is also employed by Alphadyne Asset Management. His research focuses on econometrics, high-dimensional statistics, asset pricing, and financial economics, with a strong emphasis on time series analysis and financial econometrics methodologies. Before joining CBS, he was a postdoctoral researcher in Finance at Northwestern University's Kellogg School of Management. He holds affiliations with the Center for Big Data in Finance (BIGFI) and the Center for Statistics at CBS. His educational background includes advanced studies in statistics and econometrics, though specific degrees are not detailed in the provided text. Rasmus' research interests span advanced statistical methodologies for financial data, including volatility estimation, bootstrap techniques, predictive regressions, and structural change analysis. His work has been published in top journals such as Journal of Econometrics , Journal of Financial Economics , and Quantitative Economics . A key achievement is his 2023 Econometric Theory Multa Scripsit award for prolific and impactful contributions. His publications address topics like Laplace transforms of volatility, consistent inference in predictive regressions, and dynamic hedging strategies. While no formal advisees are listed, his industry collaborations (e.g., with Nordea and Alphadyne) suggest applied research engagement. He contributes to CBS's research initiatives through participation in interdisciplinary centers focused on big data and financial statistics.
Sergio Bianchi is Professor of Quantitative Finance at Sapienza University of Rome and International Associate Professor in the Department of Finance and Risk Engineering at the Tandon School of Engineering, New York University. He previously held professorial positions at the University of Sassari, the University of Cassino, and the Pontifical Gregorian University, and has served as a visiting professor at New York University and Szent István University in Hungary. His research focuses on the stochastic modeling of financial markets, particularly through fractional and multifractional models, risk assessment, liquidity, and stochastic volatility. These areas are central to understanding market inefficiencies, volatility clustering, and long-memory behavior in financial time series. His work bridges econophysics, mathematical finance, and statistical modeling, contributing to both theoretical and applied finance. The 15 most recent publications reflect a consistent trajectory in modeling financial dynamics using advanced mathematical tools, especially fractional calculus and multifractal analysis. They span topics such as VIX option pricing, liquidity risk, Hurst exponent estimation, and market inefficiency, demonstrating a deep engagement with both empirical data and theoretical frameworks. The keywords highlight intersections with machine learning, risk management, and econophysics, showing interdisciplinary reach. Editorial and Professional Contributions: Guest Editor for journals on fractal models in economics and finance Associate Editor, Frontiers in Applied Mathematics and Statistics Associate Editor, Risk and Decision Analysis Associate Editor, Mathematical Methods in Economics and Finance Permanent member, Scientific Board, Mathematical and Statistical Methods for Actuarial Sciences and Finance (biennial conference) Sergio Bianchi has advised numerous graduate students and researchers in quantitative finance, though specific names are not listed. His editorial roles and extensive publication record suggest active mentorship and collaboration. He has been involved in research projects related to econophysics and complexity science, including participation in the Econophysics Colloquium 2024 hosted by the Complexity Science Hub. While specific grants are not mentioned, his sustained output and international collaborations indicate significant research support. He is associated with research groups working on complexity in financial systems, particularly through his involvement in events like the CSH Workshop on Complexity Science. His work continues to influence the application of fractal and stochastic models in finance, with future research likely to explore machine learning integration, high-frequency data analysis, and systemic risk modeling.
Matteo Marsiliمشاهده پروفایل
پژوهشگر
Matteo Marsili is a Senior Research Scientist at the Quantitative Life Sciences Section of the Abdus Salam International Centre for Theoretical Physics (ICTP) in Trieste, Italy. He holds a PhD from SISSA, Trieste (1994) and has held postdoctoral positions at Manchester University, Fribourg University (Switzerland), and SISSA. He joined ICTP in 2002, initially in the Condensed Matter and Statistical Physics (CMSP) Section before transitioning to the Quantitative Life Sciences Section. His research spans interdisciplinary domains, applying statistical physics to complex systems. Key areas include non-equilibrium statistical mechanics, critical phenomena, quantitative finance, statistical inference, machine learning, systems biology, and neuroscience. He investigates how collective behaviors emerge from interactions among simple units—such as particles, neurons, or financial traders—using tools from probability, information theory, and thermodynamics. His recent publications (2020–2025) show a strong focus on information-theoretic approaches to learning, relevance quantification, deep learning, and optimal inference. Themes include Bayesian modeling, minimal complexity, self-organized criticality in neural networks, and thermodynamics of information in financial markets. His work frequently appears in journals like Physical Review E , Journal of Statistical Mechanics , Physics Reports , and PLoS ONE . Matteo Marsili has collaborated extensively with researchers such as Y. Roudi, R.J. Cubero, J. Song, and R. Xie, suggesting active mentorship and team leadership. While no formal awards are listed, his sustained publication record in high-impact journals reflects significant scientific contributions. His lectures at institutions like the Kavli Institute and IHÉS further highlight his academic influence.
Andrea Rapisarda is a Full Professor of Theoretical Physics at the University of Catania, Italy, affiliated with the Department of Physics and Astronomy "Ettore Majorana" and INFN Catania. He is the coordinator of the PhD program in Complex Systems for Physical, Socio-economic and Life Sciences and serves as co-director of the International School on Complexity at the Ettore Majorana Foundation in Erice. Additionally, he is an External Faculty member at the Complexity Science Hub Vienna, reflecting his international academic engagement. His research spans Complex Systems, Statistical Mechanics, Deterministic Chaos, Nonlinear Dynamics, Complex Networks, and Agent-Based Models , with a strong emphasis on applications to socio-economic systems. He investigates the role of randomness, luck, and inefficiencies in hierarchical organizations, drawing from interdisciplinary methods in physics and computational modeling. His work has demonstrated how random strategies can outperform merit-based ones in organizational efficiency and democratic design. The 15 most recent publications highlight a consistent focus on modeling randomness in success, inequality, and decision-making across domains such as sports, economics, politics, and public health. His work integrates agent-based simulations, network analysis, and statistical physics to understand complex phenomena, often with policy-relevant implications. Ig Nobel Prize for Management (2010) Ig Nobel Prize for Economics (2022) Rapisarda is an active advisor and researcher, though specific student names are not listed in the provided texts. He has contributed to numerous editorial boards, including Physica A, Entropy, Frontiers in Physics (Social Physics) , and Complexity . He has also been involved in public outreach, policy discussions on democratic reform via sortition, and science communication through media interviews and public lectures. He leads research projects on improving organizational and societal systems using insights from complexity science.
Federica Lo Verso is a researcher at the Department of Mathematics and Computer Science, University of Southern Denmark, specializing in polymer science and nanoparticle research. Her work bridges computational modeling and experimental validation in the synthesis and structural analysis of microgels and polymer nanoparticles. Key affiliations: IMADA (Interdisciplinary Centre for Advanced Materials Simulation), eScience Center Research Focus: Her research explores polymer chain behavior, nanoparticle formation, and nanocomposite dynamics. Keywords include polymer science, nanotechnology, and condensed matter physics, with specific attention to microstructure optimization and topological constraints. Selected Publications (2016-2018): Recent work analyzes microgel deswelling, chain stiffness effects, and solvent-driven nanoparticle structuring. Trends highlight computational approaches to polymer self-assembly and nanomaterials engineering. Collaborative Network: Frequent collaborations with researchers like Arbe, Colmenero, Pomposo, and Moreno across institutions, focusing on interdisciplinary studies of polymer-based nanomaterials.
- Mechanical Engineering
- Materials Science
- Tribology
- +۳ مورد دیگر
Ramin Aghababaei serves as an Associate Professor and Head of the Mechanics and Materials section at the Department of Mechanical and Production Engineering within Aarhus University's College of Engineering. His research focuses on mechanical engineering and materials science applications. Full Name: Ramin Aghababaei Academic Rank: Associate Professor University: Aarhus University College: College of Engineering Department: Department of Mechanical and Production Engineering Research Interests : Mechanical Engineering Materials Science Tribology Computational Modeling Adhesion Mechanics Friction Analysis Publication Trends : His recent work explores wear mechanisms, tool optimization, and computational modeling of material behavior, with applications in manufacturing and sustainable materials. Contact Information : ra@mpe.au.dk | +45 93 50 89 56
Jinghao Liمشاهده پروفایل
استادیار
Jinghao Li is a Tenure Track Assistant Professor at the Department of Mechanical and Production Engineering within Aarhus University's College of Engineering. His research focuses on additive manufacturing, microstructure engineering, and solidification processes. Primary expertise in Additive Manufacturing and Powder Material Science Active in experimental and computational materials design Contact: jli@mpe.au.dk | +45 87 15 17 59 Recent publications highlight his work in: Laser wire directed energy deposition of titanium alloys High-entropy alloy design using CALPHAD and machine learning Thermoelectric property enhancement in half-Heusler compounds His research combines experimental validation with computational modeling to advance manufacturing and materials science.
Arthur Matsuo Yamashita Rios De Sousa is an Assistant Professor in the School of Computing at the Tokyo Institute of Technology, where he conducts interdisciplinary research bridging applied mathematics, statistical physics, and data science. His work focuses on the modeling and analysis of complex systems through stochastic processes, time series analysis, and network science. His primary research interests include: Applied Mathematics and Probability Theory Statistical Physics and Econophysics Complex Systems and Network Science Time Series Analysis and Forecasting Symbolic Dynamics and Entropy-Based Methods Power-Law and Heavy-Tailed Distributions His recent publications demonstrate a consistent focus on developing quantitative methods for analyzing financial time series, sales data, and other real-world complex systems. The articles span topics such as volatility modeling, network-based market analysis, multiscale entropy, and symbolic dynamics, reflecting a strong integration of theoretical physics and practical data science applications. Notable trends in his research include the use of entropy measures for regime detection, modeling of heavy-tailed phenomena in economics, and the application of complex networks to multivariate systems. These efforts contribute to both fundamental understanding and practical tools in econophysics and data-driven science. There are currently no listed scientific awards or honors in the provided text. Dr. Yamashita advises students in computational and quantitative research, particularly in areas related to data analysis of complex systems. While specific grant funding is not mentioned, his sustained publication record suggests active research support. He likely contributes to collaborative projects involving financial data modeling, nonlinear dynamics, and interdisciplinary applications of statistical physics. He is involved in academic events such as the Econophysics Colloquium and workshops at the Complexity Science Hub, indicating participation in international research networks focused on complex systems science.
Jens Christian Moesgaard Rauhe is an Associate Professor and Head of Department at the Department of Materials and Production , Aalborg University . His work focuses on composite materials and polymer foam mechanics. Research Interests: Composite materials with carbon fibers and epoxy matrices Thermal conductivity in polyurethane foams Mechanical properties of closed-cell foams Self-sensing damage detection in composites Nanomaterial reinforcement strategies Structural grading in polymeric materials Recent Publications show expertise in polyurethane composites, carbon fiber sensing, and foam mechanics. Scientific Collaborations include projects with Pyrz, Schjødt-Thomsen, and Jensen. Activities involve academic events like the 2022 University of Agder visit.
Marco Branderمشاهده پروفایل
پژوهشگر
Marco Brander is a Postdoctoral Researcher at the Department of Civil and Mechanical Engineering, Technical University of Denmark (DTU). His work centers on additive manufacturing applications in civil engineering and materials science, with a focus on large-scale 3D printing of concrete and composite materials. His primary research areas include: Additive Manufacturing (particularly Big Area Additive Manufacturing - BAAM) Material Science of thermosets and fiber-reinforced polymers Numerical modeling of material behavior during printing Concrete casting using 3D-printed formworks Optimization of processing parameters for fiber orientation and porosity Brander's recent publications demonstrate a strong emphasis on solving practical challenges in large-scale additive manufacturing, such as printing vertical surfaces and achieving desired material properties in composites. His interdisciplinary approach combines civil engineering with advanced materials processing. He completed his PhD at DTU (2022-2025) on Big Area Additive Manufacturing of Composites, supervised by Prof. Jesper Spangenberg. As a postdoc, he continues to collaborate within the same research group.