Full-time study

4 years

prof. Ing. Zdeněk Smékal, CSc.

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

The student is fostered to use the theoretical knowledge and experience gained through own research activities in an innovative manner. He 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 "Teleinformatics" 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 "Teleinformatics" 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.

Doctoral studies are carried out according to the individual study plan, which will prepare the doctoral student in cooperation with the doctoral student at the beginning of the study. The individual study plan specifies all the duties stipulated in accordance with the BUT Study and Examination Rules, which the doctoral student must fulfill to successfully finish his studies. These responsibilities are time-bound throughout the study period, they are scored and fixed at fixed deadlines. The student enrolls and performs tests of compulsory courses, at least two obligatory elective subjects with regard to the focus of his dissertation, and at least two elective courses (English for PhD students, Solutions for Innovative Entries, Scientific Publishing from A to Z).
The student may enroll for the state doctoral exam only after all the tests prescribed by his / her individual study plan have been completed. Before the state doctoral exam, the student prepares a dissertation thesis describing in detail the goals of the thesis, a thorough evaluation of the state of knowledge in the area of ​​the dissertation solved, or the characteristics of the methods it intends to apply in the solution. The defense of the controversy that is opposed is part of the state doctoral exam. In the next part of the exam the student must demonstrate deep theoretical and practical knowledge in the field of microelectronics, electrotechnology, materials physics, nanotechnology, electrical engineering, electronics, circuit theory. The State Doctoral Examination is in oral form and, in addition to the discussion on the dissertation thesis, it also consists of thematic areas related to compulsory and compulsory elective subjects.
To defend the dissertation, the student reports after the state doctoral examination and after fulfilling conditions for termination, such as participation in teaching, scientific and professional activity (creative activity) and at least a monthly study or work placement at a foreign institution or participation in an international creative project .

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 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 students submit the elaborated dissertation thesis to the supervisor, who scores this elaborate. The final dissertation thesis is expected to be submitted by the student by the end of the fourth year of studies.
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. Artificial Intelligence for Efficient Biosignal Analysis on Wearable Devices

   The topic focuses on researching and designing efficient biosignal processing methods using edge computing on wearable devices. The goal is to minimize latency and power consumption while analyzing EEG, ECG, and other biomarkers through optimized machine learning models in real-time.

   Tutor: Burget Radim, doc. Ing., Ph.D.

2. Deep audio synthesis and its use in audio reconstruction

   The topic is focused on finding new methods for audio signal synthesis and their application in the tasks of reconstruction of degraded audio signals. The synthesis will be based on a suitable learned parameterization of the signal (from the well interpretable of the DDSP (differentiable digital signal processing) type to abstract embeddings from U-net type networks). The goal of the research is to design methods that will efficiently generate a signal based on the constraints given by the problem being solved, whether in the form of interpolation of partially available samples in the time or time-frequency domain or even more abstract characteristics.

   Tutor: Rajmic Pavel, prof. Mgr., Ph.D.

3. 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.

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

4. Evaluation and Optimization of Directional Communications Technology in On-Demand Aerial Networks

   Recently, unmanned aerial vehicles and systems attracted attention in many contexts, such as on-demand wireless connectivity provisioning. This doctoral research topic addresses the emerging air-to-everything communication, including autonomous drone interworking over air-to-air links and robust aerial networking via air-to-ground channels. It targets to evaluate and optimize this emerging technology by contributing with efficient features to improve its performance, which notably account for specific effects and behavior of directional millimeter-wave channels. The proposed radio connectivity algorithms, system architectures, and performance evaluation frameworks are expected to become of significant value toward the development of future 5G+/6G wireless systems.

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

5. Machine learning in photonics

   Photonic systems cover a wide range of areas from data transmission, through sensors to quantum networks. Each photonic system has its own requirements for the transmission infrastructure, but also for input and output parameters. Manual optimization of large networks based on different types of signals is almost impossible. With the help of machine learning, the optimization of both the transmitted signals and the entire infrastructure can be achieved in photonic networks. Last but not least, machine learning algorithms can be used to detect and classify non-standard network behavior to minimize security risks.

   Tutor: Münster Petr, doc. Ing., Ph.D.

6. Modern fiber optic transmission systems

   Optical transmission systems are evolving very rapidly to meet the ever-increasing demands of users. In addition to data transmissions, there are also new transmissions such as exact time, stable frequency, radio over fiber, quantum signals transmission, etc. Individual types of signals have different requirements for the transmission infrastructure. Wavelength division multiplexing is now widely used to increase the capacity of optical fibers but it is necessary to address the issue of possible interference. In order to meet the requirements of future transmission systems, it is necessary to address several technical challenges, such as new optical modulation formats with high spectral efficiency, mitigation of linear and nonlinear phenomena in optical fibers, new types of optical fibers or signal amplification with minimal noise.

   Tutor: Münster Petr, doc. Ing., Ph.D.

7. Novel distributed and quasi-distributed fiber optic sensing systems

   The work focuses on the design, simulation and development of distributed and quasi-distributed fiber optic sensing systems. These systems use conventional single-mode telecommunication optical fibers, multimode fibers, polymer optical fibers (POF), microstructural fibers, multicore fibers, or other special fibers as a sensor. Using scattering phenomena (Raman, Brillouin, or Rayleigh scattering), or possibly changing the parameters of the transmitted optical signal (change in intensity, phase, polarization, etc.), it is possible to obtain information about temperature, vibration and other physical quantities along the optical fiber.

   Tutor: Münster Petr, doc. Ing., Ph.D.

8. Optical fiber infrastructure security

   Fiber optic networks have evolved rapidly in recent years to meet the ever-increasing demand for increasing capacity. Today, optical fibers are widely used in all types of networks due to not only transmission speed or maximum achievable distance but also security. Although fiber optic networks are considered completely secure, there are ways to capture or copy part of the data signal. Both imperfections of passive optical components and, for example, monitoring outputs of active devices can be used. With the advent of quantum computers, current encryption could be broken. It is therefore necessary to address the security of fiber-optic networks, analyze security risks and propose appropriate countermeasures.

   Tutor: Münster Petr, doc. Ing., Ph.D.

9. Psychoacoustics and eveluation subjectivity in audio signal processing

   Modern methods for the reconstruction of degraded audio signals rely mainly on generative models and a large amount of training data. However, systematic examination of the subjective quality of the result is not given the necessary attention. The aim of the doctoral research is to determine to what extent psychoacoustic principles contribute to the success of generative neural models, although they are not explicitly used in these models. The follow-up goal is to propose a differentiable prediction of the subjective evaluation of an audio signal, which will allow increasing the efficiency or quality of the output of methods using deep learning.

   Tutor: Rajmic Pavel, prof. Mgr., Ph.D.