Full-time study

4 years

prof. Ing. Zdeněk Smékal, CSc.

Chairman :
prof. Ing. Zdeněk Smékal, CSc.
Councillor internal :
prof. 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 the program "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. round (applications submitted from 01.04.2026 to 30.04.2026)

1. Artificial intelligence for image and image sequence analysis: new methods for analysing structures and events

   The topic is focused on research into new methods based on artificial intelligence for image and image sequence analysis with a focus on recognizing structures, patterns and events in various types of data. The topic includes the study and development of selected machine learning approaches and deep neural networks with the aim of improving their ability to work with spatial and temporal information, heterogeneous data and a limited amount of annotations. It will focus on the formulation of partial methodological improvements and their verification on various real data, including medical and clinical applications. The research will be aimed at a better understanding of the properties and limits of currently used models and at the design of more reliable methods for use in various applications.

   Supervisor: Smékal Zdeněk, prof. Ing., CSc.

2. Communication strategy for smart grids and IoT

   The aim of the work is to design the optimal strategy for the communication infrastructure of smart grids based on the analysis of suitable communication technologies and in the process of deploying IoT in smart cities and smart meters in smart grids. The prospective technologies will be evaluated via simulation in terms of data flows. The main objective will be to create a model for building communication networks and finding the optimal variant for the particular scenarios in the energy sector and IoT.

   Supervisor: Mlýnek Petr, doc. Ing., Ph.D.

3. Digital Speech Biomarkers and Their Relationship to Motor and Non-Motor Symptoms in Neurodegenerative Diseases

   Neurodegenerative diseases represent a heterogeneous group of disorders characterized by progressive loss of neuronal function, which may manifest through both motor symptoms and a wide spectrum of non-motor manifestations, including cognitive and language changes. Speech impairments are among the common manifestations of these disorders and may reflect both motor deficits of the speech apparatus and alterations in cognitive and linguistic processes. This dissertation will focus on the analysis of acoustic and linguistic speech biomarkers in individuals with neurodegenerative diseases and on studying their relationship to both motor and non-motor symptoms. The aim of the work will be to identify speech characteristics associated with different aspects of the clinical presentation of neurodegenerative diseases and to contribute to a better understanding of their diagnostic and monitoring potential. The research will be conducted in collaboration with the Applied Neuroscience Research Group of the Central European Institute of Technology (CEITEC).

   Supervisor: Mekyska Jiří, prof. Ing., Ph.D.

4. Digital Vocal Biomarkers of Progressive Changes in Prodromal Dementia with Lewy Bodies

   The prodromal phase of dementia with Lewy bodies represents a critical period during which subtle changes in cognitive and speech functions may already emerge, even though the full clinical symptomatology may not yet be fully developed. Speech analysis provides a non-invasive and potentially sensitive tool for detecting these changes. This dissertation will focus on the identification and analysis of acoustic and linguistic biomarkers that may reflect progressive changes associated with the early stages of this disorder. The aim of the work will be to characterize speech manifestations in individuals with suspected prodromal dementia with Lewy bodies and to analyze their evolution over time using longitudinal data and multimodal clinical assessments. The research will be conducted in collaboration with the Applied Neuroscience Research Group of the Central European Institute of Technology (CEITEC) and will aim to contribute to a better understanding of speech manifestations in early neurodegenerative processes and their potential application in clinical practice.

   Supervisor: Mekyska Jiří, prof. Ing., Ph.D.

5. Effective machine learning methods for acoustic cryptanalysis from keyboard recordings

   The research focuses on the investigation and design of algorithms for analyzing acoustic emissions produced during typing on mechanical and membrane keyboards. The objective is to identify the unique acoustic signatures of individual keys using real-time Convolutional Neural Networks (CNN) and recurrent models. The study includes the development of effective countermeasures, such as software-based masking noise generation or Signal-to-Noise Ratio (SNR) reduction techniques, to prevent the leakage of sensitive information.

   Supervisor: Burget Radim, prof. Ing., Ph.D.

6. Emerging Security Threats in the Field of Compromising Emanations

   This dissertation focuses on the identification and analysis of emerging security threats related to compromising emanations (TEMPEST), with particular emphasis on unconventional and previously unexplored attack vectors. The work will investigate the potential misuse of commonly available hardware, such as computer peripherals (e.g., microphones, speakers, cameras, optical components, or communication interfaces), for the unintended detection or leakage of acoustic vibrations and sensitive data.

   The research will include systematic monitoring of new threats and attack techniques, their theoretical analysis, and experimental verification under controlled laboratory conditions. Special attention will be given to the feasibility and limitations of such attacks, including the evaluation of their effectiveness in realistic scenarios. The dissertation will also explore methods for detecting these threats and propose appropriate technical and organizational countermeasures.

   The outcome of the work will be a comprehensive assessment of new risks in the area of compromising emanations, along with practical recommendations for mitigating these threats. The results will be applicable to the protection of sensitive environments, including governmental, industrial, and critical infrastructure systems.

   Supervisor: Münster Petr, prof. Ing., Ph.D.

7. Machine learning in photonics

   This dissertation focuses on the application of machine learning methods in photonic systems, including optical communications, sensing, and quantum networks. The main objective is to design algorithms for optimizing transmission parameters, system control, and adaptive configuration, where manual optimization is challenging.

   The work will also address the detection and classification of anomalous network behavior to improve reliability and security. The proposed methods will be validated using experimental data, and the results will provide recommendations for the effective use of machine learning in photonic infrastructures.

   Supervisor: Münster Petr, prof. Ing., Ph.D.

8. New Methods of Gamification in Cybersecurity Education

   The topic focuses on researching the use of gamification principles and game‑based educational mechanisms in the field of cybersecurity. The student will concentrate on investigating and designing new gamification methods that support the development of user awareness, motivation, and the acquisition of secure behavior practices. The thesis will also include a proposal for implementing these methods into cyber range platforms for creating simulation scenarios, along with their experimental validation.

   Supervisor: Martinásek Zdeněk, doc. Ing., Ph.D.

9. New methods using artificial intelligence tools for computer network security

   This topic focuses on the research and design of new methods utilizing artificial intelligence that can be applied to ensure the security of computer networks and in penetration testing. The research focuses on methods applicable to securing network infrastructure and conducting penetration tests on specialized smart home and energy sector devices. The student is expected to participate in the Institute’s research projects.

   Supervisor: Jeřábek Jan, doc. Ing., Ph.D.

10. Optimization of Distributed Cooperation and Decision-Making in Multi-Agent Systems

    The research focuses on the investigation and design of advanced algorithms for autonomous coordination and distributed problem-solving in dynamic environments. The primary objective is to develop robust communication protocols and Multi-Agent Reinforcement Learning (MARL) methods that enable agents to collaborate effectively in real-time, minimize resource-sharing conflicts, and ensure system scalability as the number of entities increases.

    Supervisor: Burget Radim, prof. Ing., Ph.D.