Full-time study

4 years

doc. Ing. Jiří Hošek, Ph.D.

Chairman :
doc. Ing. Jiří Hošek, Ph.D.
Councillor internal :
prof. Ing. Jaroslav Koton, Ph.D.
prof. Ing. Zdeněk Smékal, CSc.
prof. Ing. Jiří Mišurec, CSc.
doc. Ing. Vladislav Škorpil, CSc.
prof. Ing. Radim Burget, Ph.D.
Councillor external :
prof. Ing. Ivan Baroňák, Ph.D.
doc. Ing. Miloš Orgoň, Ph.D.
doc. Ing. Otto Dostál, CSc.

The student is fostered to use the theoretical knowledge and experience gained through own research activities in an innovative manner. He/She is able to efficiently use the gathered knowledge for the design of own and prospective solutions within their further experimental development and applied research. The emphasis is put on gaining both theoretical and practical skill, ability of self-decisions, definition of research and development hypotheses to propose projects spanning from basic to applied research, ability to evaluation of the results and their dissemination as research papers and presentation in front of the research community.

The doctor study program "Electronics and Information Technologies" aims to generate top research and development specialists, who have deep knowledge of principles and techniques used in communication and data wired and wireless networks and also in related areas and also in data/signal acquisition, processing and the back representation of user data on the level of application layer. The main parts of the studies are represented by areas dealing with information theory and communication techniques. The graduate has deep knowledge in communication and information technologies, data transfer and their security. The graduate is skilled in operation systems, computer languages and database systems, their usage and also design of suitable software and user applications. The graduate is able to propose new technology solution of communication tools and information systems for advanced transfer of information.

Graduates of theprogram "Electronics and Information Technologies" apply in particular in research, development and design teams, in the field of professional activity in production or business organizations, in the academic sphere and in other institutions involved in science, research, development and innovation, in all areas of the company where communication systems and information transfer through data networks are being applied and used.
Our graduates are particularly experienced in the analysis, design, creation or management of complex systems aimed for data transfer and processing, as well as in the programming, integration, support, maintenance or sale of these systems.

The doctoral studies of a student follow the Individual Study Plan (ISP), which is defined by the supervisor and the student at the beginning of the study period. The ISP is obligatory for the student, and specifies all duties being consistent with the Study and Examination Rules of BUT, which the student must successfully fulfill by the end of the study period. The duties are distributed throughout the whole study period, scored by credits/points and checked in defined dates. The current point evaluation of all activities of the student is summarized in the “Total point rating of doctoral student” document and is part of the ISP. At the beginning of the next study year the supervisor highlights eventual changes in ISP. By October, 15 of each study year the student submits the printed and signed ISP to Science Department of the faculty to check and archive.
Within mainly the first four semesters the student passes the exams of compulsory, optional-specialized and/or optional-general courses to fulfill the score limit in Study area, and concurrently the student significantly deals with the study and analysis of the knowledge specific for the field defined by the dissertation thesis theme and also continuously deals with publishing these observations and own results. In the follow-up semesters the student focuses already more to the research and development that is linked to the dissertation thesis topic and to publishing the reached results and compilation of the dissertation thesis.
By the end of the second year of studies the student passes the Doctor State Exam, where the student proves the wide overview and deep knowledge in the field linked to the dissertation thesis topic. The student must apply for this exam by April, 30 in the second year of studies. Before the Doctor State Exam the student must successfully pass the exam from English language course.
In the third and fourth year of studies the student deals with the required research activities, publishes the reached results and compiles the dissertation thesis. As part of the study duties is also completing a study period at an abroad institution or participation on an international research project with results being published or presented in abroad or another form of direct participation of the student on an international cooperation activity, which must be proved by the date of submitting the dissertation thesis.
By the end of the winter term in the fourth year of study the full-time students submit the elaborated dissertation thesis to the supervisor, who scores this elaborate. The combined students submit the elaborated dissertation thesis by the end of winter term in the fifth year of study. The final dissertation thesis is expected to be submitted by the student by the end of the fourth or fifth year of the full-time or combined study form, respectively.
In full-time study form, during the study period the student is obliged to pass a pedagogical practice, i.e. participate in the education process. The participation of the student in the pedagogical activities is part of his/her research preparations. By the pedagogical practice the student gains experience in passing the knowledge and improves the presentation skills. The pedagogical practice load (exercises, laboratories, project supervision etc.) of the student is specified by the head of the department based on the agreement with the student’s supervisor. The duty of pedagogical practice does not apply to students-payers and combined study program students. The involvement of the student in the education process within the pedagogical practice is confirmed by the supervisor in the Information System of the university.

1. round (applications submitted from 01.04.2026 to 30.04.2026)

1. Directional 3D connectivity in high-frequency industrial IoT systems

   Contemporary 5G-and-beyond radio communication technologies increasingly employ directional transmission to offer improved signal quality and more consistent coverage. However, the use of directionality notably at higher frequencies raises multiple research problems related to communication availability, reliability, and energy efficiency, among other aspects. These challenges are further aggravated when connectivity is required on the move and in 3D space. This thesis topic addresses the matters of 3D mobility and transmission directionality in application to industrial Internet-of-Things (IoT) systems, where a careful balance between robustness and efficiency is particularly important.

   Supervisor: Hošek Jiří, doc. Ing., Ph.D.

2. Enhancing Large Language Model Framework to Achieve Autonomous, Self-Organized, and Decentralized Operations

   This research aims to address the limitations of current Large Language Model (LLM) frameworks, such as the presence of central managers that can create single points of failure and issues related to agents conflicting and hallucinating. While existing LLM frameworks and agents perform well for simple use cases, they struggle with handling complex tasks end-to-end. The proposed approach focuses on developing efficient and reliable AI systems capable of managing complex tasks autonomously, in a self-organized and decentralized manner. As the demand for more sophisticated and scalable AI systems grows, the limitations of centralized LLM frameworks become more apparent. Centralized systems are prone to single points of failure and can struggle to efficiently manage the complexity and scale of modern AI tasks. Moreover, issues such as agent conflicts and hallucinations (i.e., generating incorrect or nonsensical information) further hinder the reliability and effectiveness of LLMs in complex scenarios. The research objectives include developing a decentralized LLM framework that eliminates single points of failure by distributing control and decision-making among multiple autonomous agents. Additionally, it focuses on enhancing communication protocols between agents to reduce unnecessary communication and improve efficiency. Moreover, integrating Knowledge Graphs (KG), for example, to improve the explainability of LLM responses and mitigate hallucinations, and implementing Reinforcement Learning (RL) techniques to train agents on optimal communication strategies and decision-making processes. Furthermore, the goal is to create a self-organizing system capable of dynamically incorporating new agents and adapting to changing environments and tasks. The research will employ a combination of theoretical and experimental approaches to achieve these objectives. This includes designing and implementing a decentralized architecture, developing and optimizing communication protocols, utilizing RL to train agents, integrating KGs into the LLM framework, and developing mechanisms for self-organization. The expected contributions of this research include a novel decentralized LLM framework that enhances robustness and scalability, improved communication protocols that reduce computational costs and increase efficiency, enhanced explainability and reliability of LLM responses through the integration of KGs, and a self-organizing AI system capable of dynamic adaptation and continuous learning. By addressing the limitations of current LLM frameworks and developing a decentralized, autonomous, and self-organized system, this research aims to pave the way for more robust, scalable, and reliable AI solutions. This thesis is conducted in cooperation with AT&T, where supervision and assistance will be provided by AT&T to leverage their technological expertise and infrastructure, further ensuring the success and impact of this research.

   Supervisor: Hošek Jiří, doc. Ing., Ph.D.