جست‌وجوی استاد و پژوهشگر

• Structural Biology
• Computational Biology
• Super-Resolution Microscopy
• +۴ مورد دیگر

Jonas Ries is a Professor at the University of Vienna, affiliated with the Department of Structural Biology and Computational Biology. His research focuses on super-resolution microscopy, cryo-electron microscopy, and structural analysis of cellular components. Department: Structural Biology and Computational Biology Specialization: Nuclear pore complex architecture, MINFLUX nanoscopy, fluorescent labeling. His recent work includes 2025 projects on mitochondrial fission during apoptosis via SMLM and MINFLUX, and developing a cost-effective MINFLUX microscope. He collaborates internationally, with a focus on nuclear pore complexes and artificial intelligence applications in microscopy. 2024 contributions highlight advancements in 3D MINFLUX excitation, dynamic structural biology, and synaptonemal complex analysis in C. elegans. His projects often involve computational modeling and high-throughput imaging. Research Trends Recent publications emphasize super-resolution techniques (MINFLUX, SMLM), PSF inverse modeling for microscope calibration, and nuclear pore complex dynamics. Subfields include apoptosis mechanisms, clathrin coat bending, and AI-driven image analysis. Academic Engagement He participated in the Dies Academicus event at the University of Vienna in 2024, indicating active involvement in academic community activities.

• Meiosis
• Arabidopsis thaliana
• DNA Repair
• +۳ مورد دیگر

Univ.-Prof. Dr. Peter Schlögelhofer is a leading academic at the Max Perutz Labs and heads the Department of Chromosome Biology . His research focuses on meiosis , DNA repair , and chromosome structure in Arabidopsis thaliana , with significant contributions to understanding homologous recombination and synaptonemal complex organization . Key research themes include: Regulation of cohesin and sororin in meiotic prophase Mechanistic interplay between ATM kinase and DNA repair pathways Role of CDKA;1 in chromosome axis assembly Distinct H2A histone variants in damage response compartmentalization His work aligns with UN Sustainable Development Goals (SDGs), particularly Climate Action via plant genome stability research. Recent projects include: Beyond Cohesion (2025–2028): Cohesin's role in chromosome compaction Genome Instability (2024–2028): Multidisciplinary DNA repair collaboration Meiosis Research Funding (2022–2026): Meiotic recombination mechanisms

Verena M. Dirschمشاهده پروفایل

استاد

• Natural Products
• Molecular Pharmacology
• Drug Discovery
• +۴ مورد دیگر

Verena M. Dirsch serves as Professor and Deputy Head of the Department of Pharmaceutical Sciences at the University of Vienna, where she leads research in molecular pharmacology of natural products. As Principal Investigator in the Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences (VDS PhaNuSpo), she supervises doctoral candidates and teaches advanced courses including Basics of Drug Discovery and Experimental Methods in Drug Discovery . Her research centers on elucidating cellular mechanisms of natural products for cardiovascular and metabolic diseases, with nuclear receptors (ROR, LXR, RXR, PPAR) and transcription factors as primary targets. Using endothelial cells, vascular smooth muscle models, and macrophage systems, her lab investigates nitric oxide regulation, cell proliferation, and cholesterol efflux pathways. This integrates pharmacognosy with modern drug discovery to develop nature-derived therapeutics. Analysis of her 15 most recent publications (2021-2025) reveals a dominant focus on nuclear receptor modulation by natural compounds. Key trends include identification of lupane triterpenoids and diosgenin as ROR inverse agonists, limonoids as LXR activators, and leoligin derivatives as NF-κB inhibitors. Research combines metabolomics, molecular modeling, and functional assays to link chemical structures to biological activity in metabolic syndrome and atherosclerosis. Professor Dirsch actively mentors PhD students Lorenza Bertaina, Christine Coffey, and Patricia Haiss. Her research is supported by grants including Der RXR-Ligand LRK071 (2022-2025), TMC-VASC (2012-2014), and Drugs from Nature Targeting Inflammation (2008-2011), demonstrating sustained funding in natural product pharmacology. Her laboratory collaborates with phytochemistry and ethnopharmacology groups to identify bioactive compounds, using cell-based screening and in silico tools. Current projects target vascular protection through nuclear receptor modulation, with ongoing work extending through 2028 including international conference presentations on ROR pharmacology.

• Business Administration
• Organizational Behavior
• Human Resource Management
• +۳ مورد دیگر

Oliver Fabel is a Professor and Deputy Head of the Institute for Business Decisions and Analytics at the University of Vienna, actively engaged in teaching and research within business administration. His institutional affiliation centers on organizational and personnel management studies at the university's Vienna campus. His research spans critical business disciplines including: Organizational Behavior Human Resource Management Personnel Economics Business Analytics Decision Making with emphasis on optimizing organizational structures, personnel decisions, and resource allocation strategies to enhance corporate performance and employee welfare. Professor Fabel teaches foundational and advanced courses across multiple semesters including Corporate Management, Organization and Personnel, and Personnel Economics at Master's and PhD levels. His leadership role as Deputy Head of the Institute for Business Decisions and Analytics demonstrates significant institutional responsibility and academic influence within the university's business education framework.

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +۳ مورد دیگر

Herbert Egger is a Professor of Numerical Analysis and Scientific Computing at Johannes Kepler University (JKU) Linz and heads the Institute of Numerical Mathematics. He is also a Group Leader at RICAM (Johann Radon Institute for Computational and Applied Mathematics) and Scientific Director at the Austrian Academy of Sciences. His research focuses on advanced numerical methods for partial differential equations, including structure-preserving discretizations, inverse problems, and computational electromagnetics. Current projects include SFB F90-N (CREATOR) for electric machine co-simulation, SPP 2256 for phase-field modeling in additive manufacturing, and TRR 146 on multiscale soft matter systems. Key research areas: mixed/hybrid finite element methods, energy-based modeling, model order reduction, radiative transfer, and inverse problem stabilization. His recent work emphasizes nonlinear magnetostatics, phase separation models, and efficient solvers for time-dependent systems. Egger collaborates internationally with institutions like TU Eindhoven and TU Darmstadt.

Theresa Madreiterمشاهده پروفایل

مدرس

• Knowledge-based maintenance
• Predictive and prescriptive maintenance
• Knowledge discovery from text
• +۵ مورد دیگر

Theresa Madreiter is a Lecturer & Doctoral Student at the Institute of Management Sciences within the Faculty of Mechanical Engineering and Industrial Management at Vienna University of Technology (Technische Universität Wien). Her research focuses on Production and Maintenance Management, where she combines engineering expertise with data science approaches to advance industrial maintenance practices. Her educational background includes: Dipl.-Ing. in Industrial Engineering and Mechanical Engineering from the Faculty of Mechanical Engineering and Industrial Management, Vienna University of Technology BSc. in Industrial Engineering and Mechanical Engineering from the Faculty of Mechanical Engineering and Industrial Management, Vienna University of Technology Madreiter's research interests center on knowledge-intensive approaches to industrial maintenance. She explores how knowledge-based maintenance , predictive and prescriptive maintenance systems , and knowledge discovery from text can transform traditional maintenance practices. Her work leverages semantic technology and Natural Language Processing to extract valuable insights from maintenance documentation, and applies predictive data analysis and machine learning techniques to anticipate equipment failures before they occur. This interdisciplinary approach bridges the gap between industrial engineering and data science, positioning her at the forefront of Maintenance 4.0 research. Her publications demonstrate a strong focus on applying text mining and AI techniques to industrial maintenance challenges. The trend in her work shows increasing sophistication in combining multiple data sources (both structured sensor data and unstructured text documentation) to create comprehensive maintenance solutions. Her research spans both theoretical development of algorithms and practical implementation in manufacturing environments, with a particular emphasis on discrete manufacturing systems. Madreiter's scientific achievements have been recognized with several prestigious awards: Schnieder Prize YOUNG MAKER 2021 from acatech Industrial Management - Thesis Award 2020 from Austrian Association for the Promotion of Business Research and Education Best Paper Award for "Combining process monitoring with text mining for anomaly detection in discrete manufacturing" at the Conference on Learning Factories 2022 As a doctoral student and lecturer, Madreiter is actively involved in academic mentoring and education. Her master's thesis on "Design and Development of a Prototype of a Text Understanding Tool for Maintenance 4.0" has served as the foundation for her current doctoral research and multiple research projects including TU-MARS, True_Usage, DigiMain 4.0, and DigiTS-ME. Beyond her formal academic role, she demonstrates significant commitment to social causes through her work with the Computerclubhouse Vienna (CCV), where she leads technology workshops for children from disadvantaged backgrounds. Madreiter is part of research teams working on the intersection of industrial engineering and data science, particularly focused on how AI and text analytics can transform maintenance practices in manufacturing. Her work connects closely with Industry 4.0 initiatives and represents an important bridge between traditional engineering disciplines and emerging data-driven approaches.

Stefan Wagnerمشاهده پروفایل

استاد

• Innovation Management
• Technological Change
• Intellectual Property
• +۳ مورد دیگر

Univ.-Prof. Dr. Stefan Wagner is a Professor of Business Administration with a focus on Innovation and Technology Management at the University of Vienna since October 2024. Previously, he held positions as Assistant Professor (2011–2012), Associate Professor (2013–2022), and Director of the PhD Program (2017) at ESMT European School of Management and Technology in Berlin. He has also served as Senior Fellow at the Berlin Center for Consumer Policy, Visiting Fellow at the University of Melbourne, and Visiting Professor at Pontificia Universidad Católica de Chile. Wagner’s research explores innovation processes, patent systems, and business strategy. His work examines how firms leverage technological recombination inventor mobility freemium pricing models patent thickets data-driven optimization to enhance competitiveness and drive technological progress. His recent publications focus on topics like bureaucracy measurement in German legislation personalized content monetization in mobile games software product bundling strategies Markush patent structures data exclusivity in pharmaceutical innovation reflecting his expertise in linking innovation research to practical business applications.

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۳ مورد دیگر

Van Quoc Huynh is a Researcher at the Institute for Application-oriented Knowledge Processing, Johannes Kepler University Linz (JKU), Austria. His work focuses on advancing machine learning and data mining techniques with emphasis on rule-based systems and symbolic artificial intelligence. His primary research interests include Machine Learning , Data Mining , and Symbolic AI , specializing in rule extraction algorithms, classification systems, and knowledge representation. He develops novel approaches for efficient pattern mining and interpretable model construction, bridging neural and symbolic paradigms. Recent publications demonstrate a clear trajectory toward hybrid neural-symbolic models for classification tasks and memory-efficient rule mining architectures. His work addresses critical challenges in scalability, parallel processing, and interpretability within knowledge discovery systems, with strong emphasis on practical implementations for complex datasets. Dr. Huynh actively contributes to FFG-funded research projects including the ongoing Automated Rule Extraction and Interpretation from Symbolic Regression Trees (2025-2026) and completed PreMoBAF (2021-2025). He engages with the academic community through conference organization (e.g., 20th International Conference on Foundations of Digital Games, 2025) and technical presentations on frequent itemsets mining. He operates within JKU's Institute for Application-oriented Knowledge Processing, which provides interdisciplinary infrastructure for applied AI research focusing on practical knowledge processing solutions.

Susanne Pechمشاهده پروفایل

دانشیار

• Public Economics
• Optimal Taxation
• Old-Age Provision
• +۴ مورد دیگر

Susanne Pech is an Associate Professor (A.Univ.Prof.) at the Department of Public Economics within the Kurt Rothschild School of Economics and Statistics at Johannes Kepler University Linz. She has held this position since October 2005, having previously served as an Assistant Professor from 1994 to 2005. As Praeses of the Study Field Economics, she oversees the recognition of economics courses and examinations at JKU Linz. Her office is located in the Keplergebäude Building, 1st floor, room K111A. Dr. Pech's primary research interests focus on Public Economics, Optimal Taxation, Old-Age Provision, and Insurance Economics . Her scholarly work demonstrates a consistent emphasis on the theoretical and practical aspects of taxation policy, particularly regarding bequests, wealth transfers, and pension systems. She has made significant contributions to understanding how tax policies affect intergenerational wealth transfers and how optimal tax structures can be designed when considering factors like tax evasion and different motivations for bequests. Her research profile shows a strong connection to Austrian economic policy contexts while maintaining theoretical rigor applicable to broader economic questions. The publication pattern indicates sustained scholarly productivity with a focus on journal articles and working papers through the Department of Economics at JKU Linz. Dr. Pech actively contributes to the academic community through teaching, currently offering multiple sections of Intermediate Microeconomics for the 2025 Winter semester across different classroom settings. Her involvement in research projects, particularly those related to labor economics and welfare state analysis, demonstrates her commitment to addressing contemporary economic policy challenges.

Olga Mula Hernandezمشاهده پروفایل

استاد

• Numerical Analysis of Partial Differential Equations
• Mathematical Foundations of Scientific Machine Learning
• High-Dimensional and Nonlinear Approximation
• +۲ مورد دیگر

Olga Mula Hernandez is a Professor of Mathematics at the University of Vienna since September 2025, holding the Chair of Computational Partial Differential Equations. Previously, she served as an Associate Professor at Eindhoven University of Technology (2022-2025) and Assistant Professor at University Paris-Dauphine (2015-2022). Her educational background includes a double master's degree in Applied Mathematics and Nuclear Engineering from École Polytechnique (France) and Escuela Politécnica (Madrid) (2005-2011), followed by a PhD in Applied Mathematics from Sorbonne University (2011-2014). She also completed a postdoctoral fellowship at RWTH Aachen (2014-2015). Professor Mula's research centers on numerical analysis of partial differential equations with data-driven integration. Her work spans mathematical foundations of scientific machine learning (including PINNs), high-dimensional nonlinear approximation for PDE solvers, data assimilation and inverse problems, numerical optimal transport, and structure-preserving numerical schemes. She develops explainable, data-efficient computational methods that leverage physical insights rather than relying on black-box machine learning approaches. Her methodologies address critical applications across 3D-printing, haemodynamics, environmental pollution modeling, epidemiology, and nuclear engineering, where accurate physics-based simulations are essential for real-world problem solving.

• Global Optimization
• Numerical Analysis
• Data Analysis
• +۳ مورد دیگر

Waltraud Huyer is a faculty member at the Department of Mathematics, Faculty of Mathematics, University of Vienna. Her office is located in Room 04.120 at Oskar-Morgenstern-Platz 1, 1090 Wien, Austria. She actively teaches courses including Numerical Mathematics and Introduction to Mathematics for both undergraduate and secondary school teacher accreditation programs. Her research spans Global and Local Optimization , Numerical Analysis , Data Analysis , Protein Folding , and Population Dynamics with structured populations. She develops advanced optimization algorithms like SNOBFIT for noisy environments and MINQ8 for quadratic programming, while applying mathematical techniques to biological problems such as protein structure prediction and age-structured population modeling. Analysis of her 14 most recent publications (1994-2018) reveals three dominant research thrusts: (1) Algorithmic development in global optimization (7 papers), including multilevel coordinate search and exact penalty functions; (2) Mathematical biology applications (5 papers), particularly in protein folding and population dynamics; (3) Computational verification methods (2 papers) for linear systems and feasibility problems. Her work consistently bridges theoretical mathematics with practical computational implementations. Her scientific contributions include widely-used algorithms implemented in MATLAB, such as the MCS global optimizer and SNOBFIT for noisy optimization, available through the University of Vienna's software repository. Huyer supervises teaching activities for foundational mathematics courses, emphasizing practical computational skills using MATLAB for numerical analysis and regression problems. Her teaching materials include detailed exercise sets with structured assessment criteria requiring both theoretical understanding and programming implementation.

• Renewable Energy Systems
• Technical Energy Management
• Emissions Reduction
• +۵ مورد دیگر

Christoph Schmidl serves as Head of the Master's program in Renewable Energy Systems & Technical Energy Management at the University of Applied Sciences Wiener Neustadt, Faculty of Technology, Institute for Sustainability. Based at the Wieselburg Campus, he leads significant research initiatives focused on sustainable energy solutions and emissions reduction. Dr. Schmidl's research spans multiple critical areas in sustainable energy, with a particular emphasis on renewable energy systems, technical energy management, and emissions reduction from heating technologies. His work integrates engineering solutions with user behavior analysis to develop practical, sustainable energy approaches that address real-world challenges in the energy transition. His recent publication trends reveal a strong focus on firewood stove emissions, energy communities, and user-centered energy solutions. The research shows increasing attention to citizen science approaches, stakeholder engagement in energy communities, and technical innovations for zero-emission heating technologies. His work bridges technical engineering with social aspects of energy consumption. Dr. Schmidl leads multiple research projects including EigenStrom2Go, Clean Tech Club, NETSE, E-Red, and RealLifeBiomass, which collectively address various aspects of sustainable energy transition. These projects involve collaborations with industry partners, research institutions, and community stakeholders to develop practical solutions for energy challenges. His work with the Clean Tech Club establishes educational pathways for young talents, particularly focusing on engaging girls in energy technology fields. The project creates innovation spaces where students can develop practical skills and solutions for the energy transition through hands-on experimentation and collaboration with industry experts.

• Educational Leadership
• Organizational Development
• Human Resources in Education
• +۱ مورد دیگر

Alexandria Bott serves as a Professor at the University of Teacher Education Tyrol (PHT), based in the Department of Personnel and Organizational Development. Her office is located at Pastorstraße 7, Building A/II, Room A.OG02.017.01 in Innsbruck, Austria, with contact handled via university phone (+43 512 59923 3209) and email. Her research concentrates on institutional effectiveness in teacher education through: Educational Leadership frameworks Organizational Development methodologies Human Resources Management systems Personnel Development strategies She focuses on optimizing administrative structures and personnel policies to enhance educational outcomes within higher education institutions.