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 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. Artificial Intelligence for Advanced Biomarker Imaging in Human Attention Monitoring

   The topic focuses on researching and designing advanced biomarker imaging methods using artificial intelligence for human attention monitoring. The goal is to integrate machine learning and sensor technologies to accurately analyze physiological signals related to cognitive state and concentration.

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

2. 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.

3. Communication infrastructure for local energy sector

   The aim of this work is to develop a Multi-RCAT (Multiple Radio and Cable Access Technology) communications infrastructure that takes advantage of both wired and wireless technologies. This infrastructure will enable testing and verification of services for the provision of flexibility, real-time production and consumption management, including smart metering. The local infrastructure will be defined by a test polygon and simulation environment that includes all communication technologies for the local energy sector.

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

4. 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.

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

5. 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.

6. Exploring Novel Techniques for Fine-Tuning Large Language Models and Enhancing Retrieval-Augmented Generation (RAG)

   Abstract: This research aims to explore and develop novel techniques for fine-tuning Large Language Models (LLMs) and enhancing Retrieval-Augmented Generation (RAG) to improve the performance, efficiency, and applicability of LLMs in various complex tasks. The proposed approach includes investigating advanced fine-tuning strategies, integrating external knowledge bases, and optimizing retrieval mechanisms to create more robust and contextually aware AI systems. Additionally, the research focuses on making fine-tuning processes more efficient and reducing computational consumption, which can lead to significant cost savings. Background and Motivation: As LLMs become increasingly prevalent in various applications, the need for efficient and effective fine-tuning techniques and advanced retrieval mechanisms becomes critical. Fine-tuning LLMs can significantly improve their performance on specific tasks, while RAG enables the models to generate more accurate and contextually relevant responses by leveraging external knowledge sources. However, current techniques often face challenges related to computational costs, scalability, and the integration of diverse knowledge sources. Reducing the computational resources required for fine-tuning can lead to lower costs and make these technologies more accessible. Research Objectives: To develop innovative fine-tuning techniques that enhance the adaptability and performance of LLMs across various domains. To optimize Retrieval-Augmented Generation (RAG) by improving retrieval mechanisms and integrating diverse external knowledge bases. To investigate the use of multi-modal data (e.g., text, images, audio) in fine-tuning and RAG processes. To create efficient and scalable methods for fine-tuning LLMs that reduce computational costs and training time, leading to significant cost savings. To evaluate the impact of different fine-tuning and RAG techniques on the performance and reliability of LLMs in real-world applications. Methodology: The research will employ a combination of theoretical and experimental approaches to achieve the outlined objectives. Key components of the methodology include: Advanced Fine-Tuning Techniques: Investigate and develop new fine-tuning strategies, such as few-shot learning, meta-learning, and transfer learning, to enhance the adaptability of LLMs. Optimized Retrieval Mechanisms: Design and implement advanced retrieval algorithms that improve the accuracy and relevance of information retrieved from external knowledge bases. Multi-Modal Integration: Explore the integration of multi-modal data in fine-tuning and RAG processes to create more contextually aware and versatile AI systems. Scalability and Efficiency: Develop methods to reduce the computational costs and training time associated with fine-tuning LLMs, such as model compression and distributed training techniques, to achieve significant cost savings. Performance Evaluation: Conduct comprehensive evaluations of different fine-tuning and RAG techniques using benchmark datasets and real-world applications to assess their impact on LLM performance and reliability. Expected Contributions: The proposed research is expected to make several significant contributions to the field of AI and LLMs: Novel fine-tuning techniques that improve the adaptability and performance of LLMs across various domains. Optimized retrieval mechanisms that enhance the accuracy and relevance of Retrieval-Augmented Generation (RAG). Integration of multi-modal data to create more contextually aware and versatile AI systems. Efficient and scalable methods for fine-tuning LLMs that reduce computational costs and training time, leading to significant cost savings. Comprehensive evaluations of fine-tuning and RAG techniques, providing insights into their impact on LLM performance and reliability. Conclusion: By exploring and developing novel techniques for fine-tuning LLMs and enhancing Retrieval-Augmented Gener

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

7. 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.

8. 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.

9. New Methods for Detection, Classification, and Parametric Analysis of Objects in Video Sequences

   This dissertation focuses on designing new artificial intelligence methods for detecting, classifying, and analyzing objects in video sequences, with an emphasis on determining their quantifiable parameters, such as material properties, wear level, volumetric characteristics, and other relevant parameters. The research will explore the selection and optimization of suitable algorithms for segmenting and classifying objects in digital images, emphasizing image processing techniques and the systematic identification and preparation of appropriate input features for machine learning models. Various approaches to visual data analysis will be tested to determine the accuracy and robustness of the proposed models in real-world applications, such as industrial inspection or automatic monitoring of various material or object inventories. The main challenge will be to develop new and efficient methods for evaluating objects based on limited visual information and to implement these methods in a computationally efficient manner. The outcome of this research will be an experimental analysis of different approaches and their comparison in terms of accuracy, robustness, and practical applicability.

   Tutor: Říha Kamil, doc. Ing., Ph.D.

10. New methods using artificial intelligence tools for penetration testing

    The topic is focused on research and design of new methods using artificial intelligence that can be used during the security testing (penetration test). The research is focused on suitable methods for web applications penetration testing, network infrastructure penetration testing, but also for penetration testing of dedicated devices such as smart meters. The participation on Department’s research projects is expected.

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

11. 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.

12. Novel Methods of Visual Interpretation of Partial Results of Deep Learning Networks

    The theme is focused on a visualisation of partial results and features inside of classification processes used by deep learning networks. The goal is understanding of feature analysis and visual interpretation of partial processes namely for image data object classifiers. Produced methods should provide image outputs for both art and analytic utilisation. The goal in artistic field is image synthesis and analytic instruments are aimed to inner processes and partial stages mapping and analysis of their influence on results.

    Tutor: Říha Kamil, doc. Ing., Ph.D.

13. 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.

14. Post-Quantum Cryptographic Protocols

    The topic aims at the analysis, design and optimization of modern post-quantum cryptographic (PQC) protocols. The research can be more focused on the one of current open problems such as post-quantum security in blockchain technology, quantum-resistant privacy-preserving methods, PQC on constrained devices, quantum-resistant hybrid methods, etc. The participation on Department’s national and international research projects is expected.

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

15. 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.

16. Research of novel methods of incomplete spatial information analysis in digital images

    The theme is focused on the research of novel methods for analysis of spatial information captured in digital images. These source data can be represented by temporal or spatial sequences eventually by a single image whereas the analysis should result from a given scene geometry.

    Tutor: Říha Kamil, doc. Ing., Ph.D.

17. Spatial Analysis of Medical Images for Optimizing Orthognathic Surgery

    The topic focuses on developing new automated methods for analyzing the structure and spatial relationships of the mandible as captured in CT scans. These methods will assist in machine-supported planning of orthognathic surgical procedures. The foundation will be the automatic detection of the mandibular canal in CT images, followed by the machine-based determination of the most suitable area for performing osteotomy. This includes identifying regions that are high-risk for potential injury to the inferior alveolar nerve and locations unsuitable for bone splitting due to material or structural properties. The practical goal of this research is to simplify and streamline the planning process for these procedures while reducing postoperative complications.

    Tutor: Říha Kamil, doc. Ing., Ph.D.

18. Tutor: Mekyska Jiří, doc. Ing., Ph.D.

19. Tutor: Slavíček Karel, doc. Mgr., Ph.D.