Information Science and Technology University of Pannonia

Overview

PhD in Information Science and Technology

The PhD programme in Information Science and Technology at the University of Pannonia prepares students for advanced research in a wide range of theoretical and applied IT fields. PhD candidates conduct independent research under the guidance of internationally recognized professors, participate in project work, and present their findings at national and international conferences. To obtain a PhD degree, students must publish at least two articles in refereed international journals.

The programme is designed to be flexible, accommodating the rapidly evolving demands of information science and its applications. Research in the school’s scientific workshops is of international standard and spans several core areas:

• Artificial Intelligence – supervised by Prof. Katalin Hangos (DSc) and coordinated by Ágnes Vathy-Fogarassy. Research focuses on the development of novel algorithms and methodologies in machine learning (reinforcement, supervised, unsupervised learning), theoretical studies on neural networks, natural language processing methods, and the application of AI methods to practical domains including healthcare, energy, vehicle dynamics, and Industry 4.0.

• Digital Reality – led by Prof. Péter Baranyi (DSc) and Prof. Cecília Sik-Lanyi (DSc). Research includes exploring human- and AI-enhanced cognitive capabilities in virtual environments, optimizing visual information representation for physiological and visuo-psychological comfort, developing navigation methods in virtual spaces, studying cognitive effects, and ensuring accessibility for users with special needs.

• Discrete Mathematics and Optimization – supervised by Prof. György Dósa (DSc) and Prof. Zsolt Tuza (DSc). Focus areas include structural and extremal studies of discrete mathematical models such as graphs and hypergraphs, algorithmic complexity of combinatorial problems, approximability of discrete optimization, and competitive ratio analysis in online scheduling.

• Dynamic Systems and Control – led by Prof. Katalin Hangos (DSc), Prof. Ferenc Hartung (DSc), Prof. Attila Magyar (DSc), and Prof. Mihály Pituk (DSc). Research includes intelligent diagnosis of complex systems, state and parameter estimation of nonlinear stochastic systems, optimal integration of renewable energy into power grids, and development of theoretical and numerical methods inspired by biological and neural network models with practical applications.

• Image and Signal Processing – supervised by Prof. Cecília Sik-Lanyi (DSc), Prof. Zoltán Kató (DSc), and Prof. Gyula Simon (DSc), coordinated by László Czúni. Research focuses on efficient methods for image retrieval, object recognition, and video analysis.

• Medical and Health Informatics – led by Prof. Ferenc Bari (DSc) and coordinated by Zoltán Juhász (PhD) and István Vassányi (PhD). Studies include brain and cardiovascular diagnostics and modeling, analysis of food input for multiple components, and development of intelligent informatics systems integrating remote diagnostics and therapeutic units.

Through this program, PhD students acquire not only deep theoretical knowledge but also extensive practical research experience, preparing them for leadership roles in academia, industry, and interdisciplinary research projects at an international level.