جست‌وجوی استاد و پژوهشگر

• Mathematics
• Number Theory
• Fractal Geometry
• +۳ مورد دیگر

Priv.-Doz. Dipl.-Ing.Dr. Paul Surer is a faculty member at the Institute of Mathematics , University of Natural Resources and Life Sciences, Vienna (BOKU). His research spans number theory, fractal geometry, and dynamical systems, with a focus on numeration systems and algebraic structures. Education: University Leoben (2005-2009), Universidade Estadual Paulista (UNESP) (2009-2011) Current Position: Associate Professor at BOKU's Institute of Mathematics (since 2014) Research Interests: Development of triangular labyrinth fractals and their geometric properties Rauzy fractals with measure-theoretic independence Innovations in substitutive number systems and coding prescriptions Representation theory for complex numbers using integer digits Applications in structural engineering via digital twin technology Publication Trends: 15 recent works (2014-2023) show expertise in fractal geometry, algebraic number theory, and interdisciplinary applications to structural analysis. Academic Service: Reviewer for journals like Bulletin of the Belgian Mathematical Society , Journal de Théorie des Nombres de Bordeaux , and Ukrainian Mathematical Journal .

Michael Affenzellerمشاهده پروفایل

استاد

• Genetic Programming
• Symbolic Regression
• Evolutionary Algorithms
• +۶ مورد دیگر

Michael Affenzeller is a Professor at the University of Applied Sciences Upper Austria, leading the HEAL AIST Center of Excellence for Smart Production with a focus on Digital Transformation. His research spans Genetic Programming, Symbolic Regression, and Evolutionary Algorithms, with applications in optimization, AI, and manufacturing automation. He has authored over 400 publications and led major projects such as HCAI (Human-Centered AI) and HEAL, emphasizing interdisciplinary collaboration. Research interests include developing advanced algorithms for dynamic optimization, prescriptive analytics, and federated learning in industrial contexts. He has contributed to projects like LOISI (Logistics Optimization in Steel Industries) and SimGenOpt2, addressing challenges in production planning and workforce optimization. Key collaborations involve institutions in Austria and beyond, focusing on smart manufacturing, logistics, and AI integration. His work bridges theoretical advancements with real-world applications, driving innovation in energy systems, crane scheduling, and predictive maintenance. Grants and projects include funding from FWF (doc.funds.connect) and Upper Austrian initiatives, emphasizing doctoral training and industry partnerships. He actively participates in conferences such as EUROCAST and PPSN, contributing to algorithmic advancements and computational theory.

• Production and Maintenance Management
• Knowledge-Based Maintenance
• Industry 4.0
• +۴ مورد دیگر

Prof. Fazel Ansari is a University Professor of Data-Driven Maintenance Management at Vienna University of Technology's Faculty of Mechanical Engineering and Industrial Management. He leads the Production and Maintenance Management Research Department and holds adjunct roles at multiple institutions including Fraunhofer Austria. His research focuses on knowledge-based maintenance strategies, Industry 4.0 transformation, and human-centered cyber-physical systems. Ansari serves on editorial boards for journals like AI and Intelligent Manufacturing, and holds leadership roles in professional organizations such as CIRP and IFAC. Education: Habilitation (venia docendi) in Industrial Engineering, TU Wien (2021) PhD in Computer Science (summa cum laude), Universität Siegen (2014) MSc Mechatronics, Universität Siegen (2008) BSc Mechanical Engineering, Universität Teheran (2005) Research interests emphasize applying AI and data science to optimize maintenance systems, enhance manufacturing sustainability, and develop reciprocal learning frameworks for human-machine collaboration. His work bridges technical systems with workforce development through digital twin applications and competence management solutions. Awards include the 2023 Best Paper Award from the University of Technology Sydney, Theta Award (2015), and multiple best paper recognitions at international conferences. Teaching responsibilities span master's and executive programs in maintenance management, production systems, and data science applications across TU Wien's Mechanical Engineering and Management Sciences faculties.

• Logistics and Supply Chain Management
• Maintenance Management
• Production Management
• +۴ مورد دیگر

Kurt Matyas is a Professor at the Institute of Management Sciences within the Faculty of Mechanical Engineering and Industrial Management at Vienna University of Technology. He leads the Logistics and Quality Management Research Group and serves as Ombudsperson for Studies and Teaching at TU Wien since 2023. Additionally, he co-directs the Executive MBA Engineering Management program with Caltech and holds leadership roles in the Austrian Association of Industrial Engineers (WING) since 2006. Education: Dipl.-Ing. in Mechanical Engineering/Business Administration (1989), Doctorate in Technical Sciences (1992), Habilitation in Industrial Engineering (2000). His research integrates logistics, supply chain management , and maintenance management with production and quality systems , emphasizing resource efficiency , digitalization , and sustainability in engineering management. Recent publications focus on prescriptive maintenance , lean maintenance , and learning factories for industrial education. He has contributed to peer-reviewed journals and refereed conferences since the 1990s, with a 2022 book on Maintenance Logistics . Teaching includes Production and Quality Management , Maintenance and Reliability courses, and digital skills training .

Linus Kohlمشاهده پروفایل

مدرس

• Production and Maintenance Management
• Knowledge-Based Maintenance
• Predictive & Prescriptive Maintenance
• +۵ مورد دیگر

Linus Kohl is a part-time Lecturer at the Institute of Management Sciences within the Faculty of Mechanical Engineering and Industrial Management at Vienna University of Technology. His research focuses on Knowledge-Based Maintenance , Predictive & Prescriptive Maintenance , and applications of Semantic Technology and Natural Language Processing (NLP) in industrial contexts. Academic Rank: Lecturer (part-time) Specialization: Industrial Engineering, Machine Learning, AI-enhanced Maintenance His recent publications highlight trends in Federated Learning , Text Mining , and Digital Lifecycle Asset Optimization , emphasizing AI integration for industrial systems. Kohl earned the Industrial Management - Thesis Award 2019 for his work on prescriptive maintenance models.

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.