جست‌وجوی استاد و پژوهشگر

Eduarda Sangiogo Gilمشاهده پروفایل

پژوهشگر

• Nonadiabatic Dynamics
• Photochemistry
• Quantum Algorithms
• +۳ مورد دیگر

Eduarda Sangiogo Gil is a researcher at the University of Vienna's Faculty of Chemistry, Department of Theoretical Chemistry. Her work focuses on nonadiabatic dynamics, quantum algorithms, and computational methods for simulating photochemical processes. She has developed software tools like EXASH for exciton-based surface hopping dynamics and contributed to packages such as Newton-X and MOPAC-PI. Her research integrates quantum-classical hybrid algorithms, semiempirical methods, and advanced trajectory-based simulations. Teaching: Courses on Chemical Dynamics Simulation, Computational Methods, and Journal Club at University of Vienna (2024). Collaborations: Active in international projects involving quantum computing for dynamics and exciton models in materials. Key research areas include extending exciton models beyond Frenkel theory, incorporating quantum algorithms into surface hopping approaches, and benchmarking molecular dynamics methods. Recent work explores azo-escitalopram photoisomerization and methanimine dynamics on quantum computers. Her software contributions enable efficient simulations of multichromophoric systems and nonadiabatic processes, with applications in drug design and material science.

• Virtual Screening
• Cytochrome P450
• Small Molecule
• +۴ مورد دیگر

Johannes Kirchmair is a Professor in the Department of Pharmaceutical Sciences at the Faculty of Life Sciences. His research focuses on computational methods in drug discovery, particularly virtual screening, machine learning applications, and understanding drug metabolism. He is actively involved in projects such as the Anti-Infectives Drug Discovery initiative and the AIDD: Advanced Machine Learning for Innovative Drug Discovery program. His work addresses challenges in toxicity prediction, enzyme inhibition, and assay interference detection. Key areas include cytochrome P450 metabolism, small molecule drug design, and natural product analysis. Research interests span virtual screening methodologies, metabolite identification, and computational toxicology. He has contributed to the development of tools like E-GuARD for detecting assay interference and aweSOM for predicting metabolic sites. His collaborations emphasize cross-disciplinary approaches to advance drug discovery and chemical safety. He has delivered talks on computational methods for early drug discovery and assay interference prediction, reflecting his commitment to both academic research and practical applications. Current projects aim to leverage machine learning to tackle antimicrobial resistance and enhance drug development efficiency.

Florian Arendtمشاهده پروفایل

دانشیار

• Health Communication
• Suicide Prevention
• Public Health Campaigns
• +۶ مورد دیگر

Florian Arendt is an Associated Professor of Health Communication at the University of Vienna's Institute of Journalism and Communication Science within the Faculty of Social Sciences. He holds a Ph.D. from the University of Vienna (2013) and previously served as a postdoctoral researcher at LMU Munich. His research focuses on health communication, particularly suicide prevention, media effects on health behaviors, and global health disparities. Arendt has published over 90 articles in top-tier journals such as Communication Research and Journal of Health Communication . Research interests include optimizing public health campaigns, digital health divides, quality of health news, and media stereotypes. He combines communication science with public health perspectives, investigating topics like vaccination, depression, and substance abuse. Notable projects examine the Papageno effect in social media, algorithmic disparities in suicide information access, and historical media portrayals of suicide. Educational background: Ph.D. in Communication (University of Vienna, 2013), postdoctoral position at LMU Munich's Department of Media and Communication (2013–2018). Current role since 2018 involves teaching health communication and leading interdisciplinary research projects. Grant activities include funding for studies on suicide reporting quality, digital divide impacts, and AI-driven health communication. His work emphasizes practical applications, such as advising journalists on responsible suicide reporting and evaluating search engine algorithms for crisis information equity.

• Journalism
• Political Communication
• Media Framing
• +۷ مورد دیگر

Sophie Lecheler is a Professor in the Department of Communication at the University of Vienna's Faculty of Social Sciences. Her research focuses on media framing, political communication, disinformation, and immersive journalism. She leads major projects like the Vienna Doctoral College on Digital Humanism and the TACo project on automated content moderation. Lecheler has over 96 publications since 2010, with recent work addressing digital journalism ethics, audience engagement in immersive media, and the societal impact of AI-driven content moderation. Her academic contributions span cross-national studies on political campaigns, the role of emotions in news consumption, and the challenges of misinformation in digital ecosystems. Lecheler actively engages in public discourse through media commentary on quality journalism and science communication. She collaborates internationally, with research collaborations across Europe and beyond. Education: Not explicitly stated in text. Key Projects: Vienna Doctoral College on Digital Humanism (2024-2029), TACo (2021-2025), Emotions in Political Journalism (ongoing). Awards: None explicitly mentioned. Lecheler’s work aligns with UN Sustainable Development Goals, particularly in promoting quality education (SDG 4) and responsible consumption (SDG 12) through media literacy initiatives. Her research bridges theoretical insights with practical solutions for modern media challenges.

• High Performance Computing
• Parallel Computing
• Matrix Algorithms
• +۳ مورد دیگر

Joao Nuno Estevao Fidalgo Ferreira Alves is a dedicated researcher within the Faculty of Computer Science, actively contributing to the Research Group Scientific Computing with expertise in high-performance computing and matrix algorithm optimization. His work focuses on developing innovative solutions for European exascale supercomputing challenges. His educational background includes: Bachelor of Science (BSc) Master of Science (MSc) Mr. Alves specializes in cache-oblivious algorithms, space-filling curves, and matrix transposition techniques, addressing critical data locality challenges in large-scale parallel systems. His research bridges theoretical computer science and practical supercomputing applications, with publications in premier venues including ACM Transactions on Mathematical Software and IEEE IPDPS. Analysis of his 2022-2023 publications reveals a concentrated research trajectory in matrix operations, where he pioneered triangular space-filling curves and Hilbert curve-based blocking schemes to achieve cache-efficient in-place transposition. These contributions significantly advance scientific computing methodology for modern parallel architectures. He currently serves as co-Principal Investigator for the project 'Innovative Algorithms for Applications on European Exascale Supercomputers' (2024-2025), securing active research funding for next-generation algorithm development in the European supercomputing ecosystem. Within the Research Group Scientific Computing, Mr. Alves maintains strong international collaborations, notably with Prof. Benkner at the University of Vienna, driving cooperative research on exascale algorithm design and implementation.

• Cultural Anthropology
• Digital Literacy
• Youth Studies
• +۴ مورد دیگر

Suzana Jovicic is an academic affiliated with the Faculty of Social Sciences at an unspecified university, holding a position in the Department of Social and Cultural Anthropology . Her work focuses on youth digital practices, participatory design, and the intersection of technology with cultural contexts. She holds a BA and MSc, and has been awarded prestigious fellowships including the ESPRIT Post-Doc Fellowship (2022) and the ÖAW DOC-team Fellowship (2017). Her research explores topics such as digital literacy among marginalized youth, the role of serious games in education, and the ethnographic study of social media behaviors. She has led projects like We:Design , which empowers youth through participatory digital design. Her interdisciplinary approach bridges anthropology, computer science, and education. Jovicic has published extensively on digital ethnography, mobile technology in fieldwork, and the cultural implications of social media gamification. She actively engages in academic activities, including organizing conferences and presenting on topics like post-pandemic digital ethnography. Media contributions include public discussions on digital youth culture and social media addiction, highlighting her commitment to societal impact through research.

• Quality of Service
• Service Selection
• Heterogeneous Systems
• +۴ مورد دیگر

Peter Beran is affiliated with the Faculty of Computer Science as a Researcher specializing in distributed systems and service-oriented architectures. His primary research group focuses on Workflow Systems and Technology. His research spans Quality of Service optimization , Service Selection , and Heterogeneous Systems with emphasis on distributed databases and query execution. Key methodologies include genetic algorithms and adaptive frameworks for dynamic environments. His publication trends (2007-2013) reveal consistent contributions to Cloud-based service optimization (40%) QoS-aware distributed systems (35%) Query execution planning (25%) with notable work on grid services and cloud environments. Professional activities include conference presentations on distributed data management frameworks and query optimization techniques, demonstrating active engagement in the research community.

Christian Böhmمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning
• Clustering Algorithms
• +۳ مورد دیگر

Christian Böhm is an Associate Professor at the Faculty of Computer Science, leading the Research Group Data Mining and Machine Learning. His work focuses on clustering algorithms, density-based analysis, and graph construction. He has been active in interdisciplinary projects, including computational modeling for biomedical applications and algorithm benchmarking. Notable contributions include ADOD (Adaptive Density Outlier Detection) and DynoGraph (Dynamic Graph Construction for Nonlinear Dimensionality Reduction). His research bridges theoretical data science with practical applications in healthcare and engineering. Research Group: Data Mining and Machine Learning Key Areas: Clustering, Density Analysis, Graph Algorithms, Medical Data Analytics Collaborations: Biohybrid heart valves, computational biomechanics, and algorithmic benchmarking Recent work emphasizes deep learning integration in clustering, medical outcome prediction, and scalable graph classification. His publications span conferences like IEEE ICDM and interdisciplinary journals.

• Geoinformatics
• GIS
• Natural Language Processing
• +۳ مورد دیگر

Omid Abbasi is a Researcher at the Department of Geoinformatics (Z_GIS) within the Faculty of Digital and Analytical Sciences at the University of Salzburg. His work focuses on integrating natural language processing (NLP) with geospatial data to enhance place-based analysis and recommendation systems. Key areas include semantic similarity measures in GIS, spatiotemporal modeling, and extracting spatial information from textual descriptions. His research emphasizes bridging textual and geospatial data, particularly in applications like location-based services, urban mobility analysis, and citizen satisfaction evaluation. Recent studies address topics such as LLMs' geographical representation reliability, pandemic impacts on urban environments, and tourist behavior modeling using geotagged photos. Abbasi has contributed to over 20 peer-reviewed articles since 2015, with notable work on conceptual spaces, spatial interaction models, and agent-based approaches in geo-social networks. He collaborates with the Z_GIS team on projects involving machine learning, human-centric GIS interfaces, and mobility prediction using social media data.

Andreas Reisingerمشاهده پروفایل

دانشیار

• Biomechanics
• Bone Fracture Mechanics
• Implant Fixation
• +۴ مورد دیگر

Andreas Reisinger is an Associate Professor at the Karl Landsteiner Private University of Health Sciences, where he leads the Biomechanics Laboratory and the Biomechanics Department. His work bridges mechanical engineering and biological systems, focusing on bone mechanics, implant fixation, and computational modeling for fracture prediction. Academic Rank: Associate Professor University: Karl Landsteiner Private University of Health Sciences Department: Biomechanics Reisinger's research explores bone fracture mechanics, osteoporosis-related structural changes, and 3D printing applications in medical modeling. He investigates stress distribution in post-implant bones and develops predictive tools for screw fixation stability using CT imaging and finite element analysis. Recent publications analyze multiaxial screw failure , viscoelastic soft tissue reproduction , and postoperative fracture risk after nail removal . His studies often involve collaborations with orthopedic clinics and medical imaging specialists, emphasizing clinical translation. He also contributes to medical education , teaching mathematics and informatics to medical students while integrating biomechanical principles into curricula. Current projects include the BOLUS study on 3D-printed radiotherapy boluses and LAPKNOT research on laparoscopic knot safety.

• Metabolomics
• Bioanalytics
• Plant Sciences
• +۳ مورد دیگر

Dr. Bernhard Klaus Seidl M.Sc. is a researcher at the Institute of Bioanalytics and Agro-Metabolomics, part of the Department of Agricultural Sciences at the University of Natural Resources and Life Sciences, Vienna (BOKU). He is affiliated with BOKU-Core Facilities, contributing to high-quality metabolomics research. Seidl’s research focuses on Untargeted and tracer-assisted metabolomics Sample preparation techniques (e.g., lyophilization) Development of software tools like CPExtract and MetInclude Method optimization for UPLC-Orbitrap-HRMS and mass spectrometry workflows His recent articles highlight trends in fungal metabolomics, plant stress responses (e.g., wheat to deoxynivalenol), and technical challenges in core facilities. Keywords span Metabolomics , Computational Biology , and Plant Science , with subfields like Secondary Metabolites , Lyophilization , and Tracer Studies . Seidl collaborates with researchers such as Clemens Doppler , Rainer Schuhmacher , and Christoph Bueschl , and presents at conferences like the Metabolomics Society and CTLS events.

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

Chris Oostenbrink is a University Professor at the Institute of Molecular Modeling and Simulation , University of Natural Resources and Life Sciences, Vienna. He has held this position since 2009, following prior roles as a University Lecturer at VU University Amsterdam (2004-2009). His academic journey includes a Dr. Nat.Sci. in Physical Chemistry from ETH Zurich (2000-2004) and MSc degrees in Chemistry and Pharmaceutical Sciences from VU University Amsterdam. Key Research Areas : Molecular dynamics simulations, free energy calculations, biomolecular modeling, soil organic matter interactions, protein-ligand binding, quantum-classical hybrid methods (QM/MM), computational drug design. Notable Contributions : Development of enhanced sampling techniques for free-energy calculations, structural refinement methods combining experimental data with simulations, and mechanistic studies of enzymes like cytochromes P450 and lytic polysaccharide monooxygenases. His scientific output spans computational biology, soil science, and pharmaceutical applications. Recent work focuses on polarizable QM/MM simulations with machine learning, free-energy methods in drug design, and molecular modeling of soil systems. He has presented extensively at international conferences, including keynote addresses at the Virapid Workshop 2024 and the Free Energy Methods in Drug Design Workshop. His scientific services include editorial roles (e.g., Associate Editor for Living Journal of Computational Molecular Science ) and peer-review activities for over 20 journals.

Martin Heldمشاهده پروفایل

دانشیار

• Computational Geometry
• Discrete Mathematics/Optimization
• Algorithms and Data Structures
• +۶ مورد دیگر

Ao. Univ.-Prof. Dipl.-Ing. Dr. Martin Held is an Associate Professor in the Department of Computer Science at the University of Salzburg. He leads the Geometric Algorithms Group , focusing on computational geometry, optimization, and geometric design applications. His research spans algorithms, data structures, and virtual reality frameworks. Born in 1962, re-established academic career at University of Salzburg (founded 1622) Office: Jakob-Haringer-Str. 2, Room 1.20, Salzburg (5020) Research impact: Computational geometry foundations for manufacturing and GIS systems As a senior faculty member in Austria's largest educational institution, Held contributes to research-based teaching across 90 degree programs. The University of Salzburg's 2,000 academics drive innovation through local/international networks, with Held's work particularly influencing geometric algorithms and computer-aided design domains. His group's work connects theoretical computer science with industrial applications.

Roman Hörbeمشاهده پروفایل

مدرس

• Artificial Intelligence in Manufacturing
• Production Planning and Control
• Quality Inspection Systems
• +۳ مورد دیگر

Roman Hörbe serves as a Lecturer and Researcher at the Institute of Industrial Engineering and Management within the Faculty of Engineering at the University of Applied Sciences Wiener Neustadt (FHWN). His work focuses on integrating artificial intelligence into manufacturing processes and production systems. His primary research interests include AI applications in production planning and control, advanced quality inspection systems using machine learning, data analytics for small and medium enterprises, and circular economy principles in product design. His work bridges theoretical research with practical industrial applications through numerous collaborative projects with manufacturing companies. Roman's research demonstrates a strong trend toward practical AI implementation in manufacturing environments, with emphasis on Convolutional Neural Networks for quality inspection, data management systems for learning factories, and production planning optimization. His publications show increasing focus on real-world applications of machine learning in high-variability production environments. Roman actively participates in multiple funded research projects including IntelliProPS (2023-2026), DigiLean (2024-2025), ProcessIQ (2023-2024), and CircularPro (2022-2023), all focusing on AI integration in manufacturing processes. These projects are primarily funded through FFG's COIN construction FH for the economy program and Innovation Camps program. He contributes to research teams focused on DigiLean workshops with food industry companies, ProcessIQ for automated defect detection in special machine manufacturing, IntelliProPS for AI-enhanced production planning concepts, and CircularPro for developing continuing education on circular design principles for SMEs.

• Algorithmic Bias
• AI Ethics
• Web Science
• +۶ مورد دیگر

Katharina Kinder-Kurlanda serves as Professor at Alpen-Adria-Universität Klagenfurt (AAU), affiliated with the Institute for Educational Science and Educational Research and the Digital Age Research Center (D!ARC). Her work bridges digital humanities, educational science, and critical technology studies, focusing on the societal implications of algorithmic systems and data practices within European contexts. Her research centers on Algorithmic Bias detection in AI systems, particularly large language models, and develops frameworks for Fairness in Machine Learning through ethnographic interventions. She investigates Privacy and Data Protection challenges in social media research, explores Open Science opportunities for researcher-subject relationships, and contributes to European AI Regulation through interdisciplinary perspectives on value alignment. Her methodology emphasizes sociotechnical approaches that uncover power dynamics in datafication processes and algorithmic governance. Recent publications reveal consistent focus on bias quantification in language models (2025), ethnographic fairness interventions in machine learning (2024), and European value alignment in AI regulation (2022). Her work demonstrates increasing engagement with Web Science conference leadership and methodological innovations in data studies, while maintaining critical attention to ethical dimensions across all research domains. She leads significant research initiatives including the Marie Skłodowska Curie Innovative Training Network NoBIAS: Artificial Intelligence without Bias (2020-2024) and the Center for Advanced Internet Studies (CAIS) project on Open Science: New Opportunities for the Relationship between Researchers and Researched Subjects? (2020-2021). Her grant portfolio reflects strategic focus on interdisciplinary training networks and ethical framework development for emerging data practices. As core faculty at D!ARC, she contributes to Klagenfurt's interdisciplinary hub for digital transformation research, collaborating across computer science, social sciences, and humanities to address contemporary challenges in algorithmic societies through team-based projects and methodological innovation.