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.

3. round (applications submitted from 01.12.2024 to 15.12.2024)

1. Using Machine Learning for Modelling of Audio Systems

   Neural networks and machine learning are currently used in the area of audio signal processing for data mining, e.g. recognition of genre, music information retrieval from recordings, etc., and speech processing, such as word recognition, speaker identification, emotion recognition, etc. However, their potential use is also in modelling of audio systems. The aim of dissertation thesis is to find algorithms for optimization of parameters of digital musical effects, algorithms for room acoustic simulation and more using machine learning and hearing models for training of neural networks. The research will focus on the static optimization of the system parameters according to the original analog system and on the dynamic change of the parameters in real time on the basis of the properties of the processed audio signal. Research will be conducted in collaboration with companies dealing with the development of software for processing audio signals.

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

1. round (applications submitted from 01.04.2024 to 30.04.2024)

1. Cybersecurity in Transport Systems

   The research will focus on the analysis of threats, vulnerabilities and security methods in Intelligent Transportation Systems (ITS), the Internet of Vehicles, inter-vehicle/intra-vehicle communication and associated digital systems and services in transportation. The work will further deal with how to secure these smart transport systems in an agile, robust and sustainable manner. The minor goal of the work is research of the protection of user privacy in ITS services. The participation on Department’s national and international research projects is expected.

   Tutor: Malina Lukáš, doc. Ing., Ph.D.

2. Improving the perceptual quality of compressed audio signals using deep neural networks

   Athough a great attention is paid to audio coding, coders with a low bit budget still produce perceptually unpleasant results. The study would be focused on the design of an generative deep neural network, which would improve the perceptual quality of the compressed files. The network's input would therefore be the compressed signal, and its output would be the perceptually improved version.

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

3. Innovative Approaches to Network Process and Topology Simplification Using Bio-Inspired AI Algorithms

   Network process and topology simplification is pivotal in a multitude of fields such as network design, system architecture, and more. Within this doctoral research, the student will delve into the exploration and development of innovative principles for network process and topology simplification. The principles will be employed on complex systems (like, 5G telecommunication networks systems etc.) to autonomously simplify and optimize their structures and processes. Drawing inspiration from nature, the student will employ bio-inspired algorithms such as Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, and others. These algorithms mimic biological phenomena like evolution, swarm behavior, and foraging patterns, providing robust solutions for complex optimization problems. These developed algorithms will be utilized across various fields, including network design, system architecture simplification, and more. The effectiveness and efficiency of these algorithms will be validated through numerical simulations and practical implementations. The research tools will primarily encompass various types of bio-inspired algorithms, both established and emerging, with potential exploration into hybrid techniques that integrate bio-inspired algorithms with other machine learning or optimization methodologies. The mission of this doctoral research is to expand the current understanding and application of bio-inspired algorithms in network process and topology simplification, paving the way for more efficient, simplified, and optimized systems across various industries.

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

4. 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.

5. 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.

6. New Principles of Anomaly Detection Using Machine Learning

   Anomaly detection is a concept widely applied to numerous domains, such as financial fraud, cyber intrusion and many others. Within this topic, a doctoral student will focus on research and development of new principles and algorithms of anomaly detection using machine learning. The proposed principles will be applied mainly on text data (normal text, network syslogs, etc.) to automatically identify anomalies in large datasets. Some of the fields, where the developed anomaly detection algorithms will be applied on, include e-mails, network performance syslogs, network security syslogs and patient symptoms data. As preferred research tools, student will primarily consider supervised and unsupervised machine learning concepts, as well as deep learning techniques. The developed algorithms will be verified through the numerical simulations as well as implementation in experimental networks.

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

7. 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.

8. Quantum key distribution secured optical networks

   Today's digital world is dependent on data security during communication but also in storage, for example in e-banking, e-commerce, e-health or e-government. With the advent of quantum computers, there is a risk of potential security breaches today. Quantum Key Distribution (QKD) provides a way to distribute and share secret keys that are necessary for cryptographic protocols. The information is coded into individual photons. Integrating QKD systems into existing network infrastructure used for telecommunications is a topical challenge. Some other major challenges include increasing of the key rate, increasing the range of the QKD system, or reducing the complexity and robustness of existing solutions.

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

9. Reconstruction of degraded audio signals using combination of deep neural networks and model-based techniques

   The thesis will deal with modern approaches to audio signal restoration, specifically focusing on the task of filling in a missing gap in an audio signal and the related task of restoration of saturated samples. Problems of this type are commonly encountered in practice (archival recordings, dropouts in VoIP calls, etc.). Current methods provide a very good interpolation of signals that are stationary and harmonic in the vicinity of the corrupted segment. While current developments in the field of deep neural networks (DNN) are promising, DNNs have been shown to improve their performance when complemented with a physical formulation of the problem (model-based networks). The study will focus on approaches that combine algorithms that have been successful in recent years (optimization-based methods) and DNNs. The work will not neglect the psychoacoustic side of the problem. (Cooperation with the Acoustics Research Institute, Vienna)

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