Academically oriented

Full-time study

4 years

prof. Ing. Lukáš Sekanina, Ph.D.

Chairman :
prof. Ing. Lukáš Sekanina, Ph.D.
Councillor internal :
doc. Ing. Jan Kořenek, Ph.D.
prof. Dr. Ing. Jan Černocký
prof. Ing. Adam Herout, Ph.D.
prof. Ing. Tomáš Hruška, CSc.
doc. Dr. Ing. Petr Hanáček
prof. Dr. Ing. Pavel Zemčík, dr. h. c.
prof. RNDr. Alexandr Meduna, CSc.
prof. Dr. Ing. Zbyněk Raida
prof. RNDr. Josef Šlapal, CSc.
prof. Ing. Pavel Václavek, Ph.D.
prof. Ing. Tomáš Vojnar, Ph.D.
Councillor external :
prof.,RNDr. Jiří Barnat, Ph.D.

The goal of the doctoral degree programme is to provide outstanding graduates from the master degree programme with a specialised university education of the highest level in certain fields of computer science and information technology, including especially the areas of information systems, computer-based systems and computer networks, computer graphics and multimedia, and intelligent systems. The education obtained within this degree programme also comprises a training and attestation for scientific work.

• Graduates from the doctoral study programme are trained to independently work in research, development, or management.
• They are able to solve and/or to lead teams solving advanced conceptual, research, development, or production problems in the area of contemporary information technology and its applications.
• They can be engaged to work on creative tasks, to lead research and development teams, or to work in management of companies or organizations whenever there are required abilities to work in an independent and creative way, to analyze complex problems, and to propose and realize new and original solutions. Graduates from the doctoral study programme can also teach and/or scientifically work at universities.

FIT graduates in general and FIT doctoral graduates in particular do not have a problem finding employment at scientific, pedagogical or management positions both in Czech Republic and abroad.

• FIT   graduates of the doctoral study are capable of independent scientific, research and management work in the field of Informatics, Computer Technology and Information Technologies. Graduates are ready to solve challenging conceptual, research and development problems. They can independently conduct research, development and production in the field of modern information technology.
• Typically, they work as creative workers at top scientific research workplaces, as leaders of research and development teams and in scientific and pedagogical work at universities. Graduates of this program are also employed in higher functional positions of larger institutions and companies, where the ability to work independently, analyze complex problems and design and implement new, original solutions is required.
• And, last but not least, graduates typically continue as so-called "postdoc" in their academic careers in Czech Republic or abroad.

The requirements that the doctoral students have to fulfil are given by their individual study plans, which specify the courses that they have to complete, their presupposed study visits and active participation at scientific conferences, and their minimum pedagogical activities within the bachelor and master degree programmes of the faculty. A successful completion of the doctoral studies is conditional on the following:

• The student has to pass a doctoral state examination within which he/she has to prove a deep knowledge of methodologies, theories, and their applications in accordance with the state of the art in the areas of science that are given by the courses included in his/her individual study plan and by the theme of his/her future dissertation thesis. The doctoral state examination also encompasses an evaluation of the presumed goals of the future dissertation thesis of the student, of the chosen solution method, and of the so far obtained original results.
• The student has further to prepare and defend his dissertation thesis.

The rules are determined by the directions of the dean for preparing the individual study plan of a doctoral student.  The plan is to be based on the theme of his/her future dissertation thesis and it is to be approved by the board of the branch.

• obligatory doctoral study programme Courses, the total number of courses a student has to complete and their mapping into particular semesters.
• a Research Plan Content (brief descrition of research content - focuse at the intended research area and the doctoral thesis topic
• a Research Plan ( list of research activities focused at the intended research area and the doctoral thesis topic - conferences and seminars to be attended , work to be published)
• teaching duty according to BUT study rules and regulations
• doctoral study schedule

Brno university of technology provides studies for persons with health disabilities according to section 21 par. 1 e) of the Act no. 111/1998, about universities and about the change and supplementing other laws (Higher Education Act) as amended, and according to the requirements in this field arising from Government Regulation No. 274/2016 Coll., on standards for accreditation in higher education, provides services for study applicants and students with specific needs within the scope and in form corresponding with the specification stated in Annex III to Rules for allocation of a financial contribution and funding for public universities by the Ministry of Education, Youth and Sports, specifying financing additional costs of studies for students with specific needs.

Services for students with specific needs at BUT are carried out through the activities of specialized workplace - Alfons counselling center, which is a part of BUT Lifelong Learning Institute - Student counselling section.

Counselling center activities and rules for making studies accessible are guaranteed by the university through a valid Rector's directive 11/2017 concerning the status of study applicants and students with specific needs at BUT. This internal standard guarantees minimal stadards of provided services.
Services of the counselling center are offered to all study applicants and students with any and all types of health disabilities stated in the Methodological standard of the Ministry of Education, Youth and Sports.

The study programme builds on both the ongoing follow-up Master's programme in Information Technology and the new follow-up Master's programme in Information Technology and Artificial Intelligence.
Students can also, according to their needs and outside their formalized studies, take courses and trainings related to the methodology of scientific work, publishing and citation skills, ethics, pedagogy and soft skills organized by BUT or other institutions.

3. round (applications submitted from 21.09.2026 to 31.01.2027)

1. Advanced algorithms of Video, Image, and/or Signal processing

   The topic concerns algorithms of image, video, and/or signal processing. Its main goal is to research and in-depth analyze existing algorithms and discover new ones so that they have desirable features and so that they are possible to efficiently implement. Such efficient implementation can be but does not necessarily have to be part of the work but it is important to prepare the algorithms so that they can be efficiently implemented e.g. in CPU, in GPU, in AI accelratorrs, embeded systems, even in combination with FPGA, in extremely low power systems, or in other environments. It is possible to exploit algorithms of artificial intelligence, such as neural networks, especially CNNs, LLM/VLM. The application possibilities of the algorithms are also important and the application can be but does not have to be part of the work. The algorithms/applications of interest include:

   • recognition of scene contents, events, and general semantics of video sequences (such as identification of traffic situations, identification in scenes in moview, action identification, etc.),
   • classification of video sequences using machine learning (AI) through deep convolution networks neural network or similar approaches (e.g. for industrial quality inspection, object of scene characteristics search, etc.), possibly in combination with object tracking in video using modern methods,
   • parallel analysis of video and signal (e.g. for detection of coincidence of occurrence of object in video and characteristic signal shape in surveillance applications), fusion of video and sognals,
   • modern algorithms of video, image, and/or signal exploiting "client/server" or "cloud" approaches suitable e.g. for mobile technology and/or embedded systems,
   • algorithms of video compression and analysis through frequency or wavelet transforms or similar methods...

   After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

   Collaboration on grant projects, such as TACR, MPO, H2020, ECSEL (employment expected).

   Supervisor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

2. Automated Techniques for Requirements-Based Test Assessment

   Evaluation of the completeness of test suites with respect to software requirements represents a fundamental challenge in the verification of critical systems, particularly in regulated domains. Current practice is largely based on manual assessment, which is time-consuming and error-prone. This dissertation will focus on the research of formal and automated methods for analyzing requirement coverage by test cases, with an emphasis on the semantic aspects of requirements and their implicit behavior.

   The goal of the dissertation is to propose a formal framework for representing requirements and test cases, and to define objective coverage criteria derived from the semantics of requirements (e.g., combinations of conditions, temporal constraints, sequences of actions, or state transitions). On this basis, methods for computing the coverage level of a test suite and for identifying uncovered semantic cases will be investigated.

   The research will focus in particular on the following areas:

   • design and systematic comparison of formal models suitable for representing different classes of requirements and testing artifacts, including analysis of their expressive power and analyzability;
   • formalization of the notion of requirement coverage and derivation of corresponding coverage obligations from the formal semantics of requirements;
   • design of algorithms for analyzing test suite coverage over formal representations, including identification of uncovered cases and their linkage to specific parts of the requirements;
   • investigation of methods for transforming informal requirements and test cases into formal models, including the use of natural language processing techniques and large language models, with emphasis on the reliability and limitations of these approaches;
   • experimental evaluation of the proposed methods on realistic datasets, including assessment of accuracy, completeness, and scalability.

   The dissertation will also include the design and implementation of prototype tools supporting selected aspects of the proposed approach, along with their validation on examples from embedded and avionics systems. Emphasis will be placed on integrating formal methods, software testing, and automated analysis techniques, with the aim of contributing to systematic and repeatable evaluation of test suite quality.

   Supervisor: Rogalewicz Adam, doc. Mgr., Ph.D.

3. Detection and Prevention of Deepfake Attacks on Images of Human Faces

   The student will study techniques used in deepfake attacks on images and videos of human faces. The doctoral study is linked to the TrustedFace project (in collaboration with Innovatrics). The goal is to investigate existing deepfake attacks on images of human faces aimed at impersonating identities, quantify their severity, and propose possible defenses – both for detecting attacks and preventing them. The student will need to gain in-depth familiarity with available datasets of human faces, techniques for face identification and verification in images and video, and deepfake attack techniques. The ultimate goal of the study and doctoral work is to develop defenses against deepfake attacks and to formulate recommendations for systems that utilize face-based identification and verification.

   Supervisor: Herout Adam, prof. Ing., Ph.D.

4. Efficient Technology for Dealing with Logics and Automata (Not Only) for Formal Analysis and Verification

   Different types of logics and automata are among the most fundamental objects studied and applied in computer science for decades. Nevertheless, there are many unsatisfactorily solved problems in this area, and new, exciting problems related to ever new applications of logics and automata are constantly emerging (e.g., in the formal verification of finite and infinite state systems with various advanced control or data structures, in decision procedures, in program or hardware synthesis, in quantum program verification, or even in methods for efficient search in various types of data or network traffic).

   The subject of the dissertation will primarily be the development of the state of the art in efficient work with various logics (e.g. over pointer structures, strings, various arithmetic, temporal logics, etc.). To this end, approaches based on different types of decision diagrams, automata, but also e.g. approaches based on the existence of a model of bounded size or on efficient reductions between different types of logical theories will be investigated. In connection with this, methods for efficient work with decision diagrams and different types of automata (automata over words, trees, infinite words, automata with counters, registers, etc.) will be developed. The work will include theoretical research as well as prototype implementation of the proposed techniques and their experimental evaluation.

   The work will be solved in collaboration with the VeriFIT team working on the development of techniques for working with logics and automata and their applications at FIT BUT. There is also the possibility of close cooperation with various foreign partners of VeriFIT: Academia Sinica, Taiwan (Prof. Y. Chen); TU Vienna, Austria (Assoc. F. Zuleger); LSV, ENS Paris-Saclay (Assoc. M. Sighireanu); IRIF, Paris, France (Assoc. A. Bouajjani, Assoc. P. Habermehl, Assoc. C. Enea), Verimag, Grenoble, France (Assoc. R. Iosif); Uppsala University, Sweden (prof. P.A. Abdulla, prof. B. Jonsson); or RPTU, Kaiserslautern, Germany (prof. A.W. Lin).

   Supervisor: Lengál Ondřej, doc. Ing., Ph.D.

5. Embedded Systems for Video/Signal Processing

   The topic focuses embedded image, video and/or signal processing. Its main goal is to research capabilities of "smart" and "small" units that have such features that allow for their applications requiring smyll, hidden, distributed, low power, mechanically or climatically stressed systems suitable of processing of some signal input. Exploitation of such systems is perspective and wide and also client/server and/or cloud systems. The units themselves can be based on CPU/DSP/GPU/AI accelerator, programmable hardware, or their combination. Smart cameras can be considered as well. Applications of interest include:

   • classification of images or objects using machine learning (AI) using traditional methods or through deep convolution networks neural network or similar approaches (e.g. for industrial quality inspection, etc.),
   • parallel analysis of signal(s) and video (e.g. for robust detection of occurrence of object in industrial or surveillance applications),
   • modern algorithms of video, image, and/or signal exploiting "client/server" or "cloud" (with focus on the technlogy) suitable e.g. for mobile technology and/or embedded systems,
   • other similar topics can be individually consulted and considered.

   A possibility exists in collaboration on grant projects, especially the newly submitted TAČR, HE, ECSEL ones (employment expected).

   Supervisor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

6. Evaluating Vulnerabilities and Capabilities of Large Language Models

   Despite the impressive performance of large language models (LLMs), these models are still not well understood. A lot of effort is dedicated to evaluating the capabilities of LLMs and creating guardrails to prevent potentially harmful behaviours. However, small prompt variations still significantly alter the behaviour of LLMs, they often fail in tasks that are similar to the ones seen in training data but contain a small change, or they can be easily used to generate confident misinformation. As the large language models increasingly mediate access to information and are increasingly used by expert and non-expert users, it is essential to develop rigorous methods for evaluating their true capabilities and identifying the sources of their vulnerabilities.

   The goal of this research is to build a deeper and more reliable understanding of the LLM behaviour. The first possible research direction involves designing a benchmark that can accurately measure both advanced capabilities (such as reasoning, planning, understanding, math or multilingual capabilities) and common failure modes and vulnerabilities (including hallucination, brittleness, tendency or ease of generating problematic content such as misinformation). The second possible research direction leverages mechanistic interpretability to study the internal structures that drive the LLM behaviours. These tools will be used to investigate why specific vulnerabilities arise, how the capabilities arise, and whether targeted interventions can reduce failures while maintaining core capabilities.

   By integrating robust behavioural benchmarking with mechanistic insights, the project aims to produce a more principled understanding of how LLMs work and how they can be made safer and more reliable. The application domain includes (but are not limited to) low-resource tasks, multilingual understanding, misinformation and other problematic behaviour detection (narrative detection) or even LLM user simulation.

   Relevant publications:

   • Vykopal, I., Pikuliak, M., Srba, I., Moro, R., Macko, D., and Bielikova, M., 2023. 2024. Disinformation Capabilities of Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14830–14847, Bangkok, Thailand. Association for Computational Linguistics. https://aclanthology.org/2024.acl-long.793/
   • Zugecova, A., Macko, D., Srba, I., Moro, R., Kopal, J., Marcinčinová, K., and Mesarčík, M., 2025. Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 780–797, Vienna, Austria. Association for Computational Linguistics. https://aclanthology.org/2025.acl-long.38/

   The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with researchers from highly respected research units. A combined (external) form of study and full employment at KInIT is expected.

   Supervisor: Bieliková Mária, prof. Ing., Ph.D.

7. Formal Methods in Design and Analysis of Quantum Programs

   Development in the field of quantum computers is moving forward unstoppably. However, for the effective use of quantum hardware, it is necessary to have the right support at the software level, i.e., quantum algorithms for solving problems, as well as tools for compiling, optimizing and synthesizing quantum programs, mapping them to a specific quantum technology, mapping them to a fault tolerant protocol, analyzing, simulating, and debugging quantum programs, etc.

   The subject of the dissertation will primarily be the development of current knowledge in the field of formal methods in quantum program design. The main focus here will be on the use of automata theory, automatic reasoning, and decision diagrams. In connection with this, methods for effective work with decision diagrams and various types of automata (automata over words, trees, infinite words, automata with counters, registers, etc.) will also be developed. The work will include both theoretical research and prototype implementation of the proposed techniques and their experimental evaluation.

   The work will be carried out in collaboration with the VeriFIT team at FIT BUT, which develops techniques for working with logics and automata and their applications. There is also the possibility of close cooperation with various foreign VeriFIT partners: Academia Sinica, Taiwan (prof. Y. Chen); Uppsala University, Sweden (prof. P.A. Abdulla, prof. R. S. Thinniyam); or Leiden Institute for Advanced Computer Science, Leiden, the Netherlands (prof. A. Laarman).

   Supervisor: Lengál Ondřej, doc. Ing., Ph.D.

8. Homographic Navigation – augmented reality for capturing precise images of objects

   The aim of this work will be to develop the technique of "homographic navigation," a new concept being developed under the guidance of the doctoral program supervisor. The PhD student will develop computer vision algorithms and related machine learning techniques. Model training for homographic navigation must be minimally supervised. Both the learning algorithms and the inference and use in the application must be fast and resource-efficient. The project also involves prototyping and developing an augmented reality application that will be used for data collection, user testing, and other related tasks. Homographic navigation technology is intended to have immediate industrial applications, so the doctoral study will also include the development of all essential techniques through to the production phase.

   Supervisor: Herout Adam, prof. Ing., Ph.D.

9. Human in the loop (HIL) in the adaptation of AI systems

   This PhD topic is driven by the needs of users for which the privacy of data is crucial and concentrates on improving AI (especially speech recognition) system in case the user is able to identify and correct systems errors. The topic includes proper evaluations of HIL systems, selection of data to be proposed for correction and actual fine-tuning / adaptation techniques working with large pre-trained models. 

   Supervisor: Černocký Jan, prof. Dr. Ing.

10. Modern Algorithms of Computer Graphics

    The topic concerns algorithms of computer graphics and image synthesis. Its main goal is to research new algorithms so that their features and application possibilities are better understood so that they are improved or newly created. If suitable, it is possible to work on various platforms, includeing parallel CPUs, such as x86/64, ARM, Xeon PHI, GPU, etc. or other cores in CUDA, OpenCl, VHDL, etc. Algorithms of interest include:

    • rendering using selected computer graphics methods (such as ray tracing, photon mapping, direct rendering of "point clouds", etc.),
    • modeling of scenes and redering using artificial intelligence, including image synthesis using neural netowrks (especially CNNs),
    • processing and rendering of "lightfield" images, their acquisition, or possibly compression, reconstruction of 3D scenes from images and/or video, eventually also fusing with other sensors, such as LIDAR or RADAR,
    • modern algorithms of geometry suitable for applications in cpmputer graphics and perhaps also 3D printing,
    • emerging algorithms of 3D synthesis, holography, wavelet transform, frequency transform, etc.

    After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

    Collaboration on grant projects, such as TACR, HE, ECSEL (employment expected).

    Supervisor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

11. Non-Traditional Visal Computing Algorithms

    The topic of the work includes algorithms of comptuer graphics, image, and video, i.e. "Visual Computing", such as HDR (High Dynamic Range) image, multispectral image, stereoimage, possibly also image augmented with material features, temperature, etc. The goal is to understand the features and possibilites but also applications, to analyze the algorithms in-depth, prepare new and improve them. The possible algorithms include:
    
    

    • Acquisition of HDR image and video, its compression, assembling from several standard images, videos, etc.,
    • acquisition, processing, and visualization of mutispectral images (images with more than three spectral components),
    • processing of stereo images and other images containing the depth information using both structural light illumination or no structural light,
    • algorithms suitable for mobile technology and/or embedded systems focusing the above types of iamges, or images acquired from drones,
    • metods of visualization of HDR images and video, tone-mapping, real-time tone mapping,
    • algorithms and applications exploiting Wavelet, frequency, and/or other similar transformations.

     

    After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

    Collaboration on grant projects, such as TACR, MPO, HE, ECSEL (employment expected).

    Supervisor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

12. Performance of voice deepfake detection by humans and machines

    The work will start with getting familiar with the basics of the problem of voice deep fake detection (DFD), terminology, available techniques, data and challenges (AVSpoof, WildSpoof), with the history and state of the art techniques and tools for speaker recognition (wespeaker toolkit), with state of the art techniques and tools for personalized text to speech (pTTS) synthesis and voice conversion. The topic will then concentrate on the evaluation of human and machine performance in DFD, while concentrating on the motivation aspect of human subjects to simulate true attacks on people. The PhD will then progress in both (1) technical (advances in deepfake generation and detection technology) and (2) human-aspects. The work counts on collaboration with specialists from psychology and sociology. 

    Supervisor: Černocký Jan, prof. Dr. Ing.

13. Performance of voice deepfake detection by humans and machines

    The work will start with getting familiar with the basics of the problem of voice deep fake detection (DFD), terminology, available techniques, data and challenges (AVSpoof, WildSpoof), with the history and state of the art techniques and tools for speaker recognition (wespeaker toolkit), with state of the art techniques and tools for personalized text to speech (pTTS) synthesis and voice conversion. The topic will then concentrate on the evaluation of human and machine performance in DFD, while concentrating on the motivation aspect of human subjects to simulate true attacks on people. The PhD will then progress in both (1) technical (advances in deepfake generation and detection technology) and (2) human-aspects. The work counts on collaboration with specialists from psychology and sociology. 

    Supervisor: Černocký Jan, prof. Dr. Ing.

14. Speech recognition and data processing for ATC-pilot communication in aviation

    This PhD topic is focused on speech recognition and data processing for ATC-pilot communication in aviation. It will cover all components of automatic speech recognition (ASR), i.e. data processing, acoustic model, vocabulary (including special aviation terminology), language model as well as interaction with sources of meta-data (radar information, airport). Special attention will be paid to the use of large pre-trained model, code-switching, reliable language identification from short segments and adaptation to the conditions of given airport with crawled data.

    Supervisor: Černocký Jan, prof. Dr. Ing.

2. round (applications submitted from 01.06.2026 to 30.08.2026)

1. Advanced algorithms of Video, Image, and/or Signal processing

   The topic concerns algorithms of image, video, and/or signal processing. Its main goal is to research and in-depth analyze existing algorithms and discover new ones so that they have desirable features and so that they are possible to efficiently implement. Such efficient implementation can be but does not necessarily have to be part of the work but it is important to prepare the algorithms so that they can be efficiently implemented e.g. in CPU, in GPU, in AI accelratorrs, embeded systems, even in combination with FPGA, in extremely low power systems, or in other environments. It is possible to exploit algorithms of artificial intelligence, such as neural networks, especially CNNs, LLM/VLM. The application possibilities of the algorithms are also important and the application can be but does not have to be part of the work. The algorithms/applications of interest include:

   • recognition of scene contents, events, and general semantics of video sequences (such as identification of traffic situations, identification in scenes in moview, action identification, etc.),
   • classification of video sequences using machine learning (AI) through deep convolution networks neural network or similar approaches (e.g. for industrial quality inspection, object of scene characteristics search, etc.), possibly in combination with object tracking in video using modern methods,
   • parallel analysis of video and signal (e.g. for detection of coincidence of occurrence of object in video and characteristic signal shape in surveillance applications), fusion of video and sognals,
   • modern algorithms of video, image, and/or signal exploiting "client/server" or "cloud" approaches suitable e.g. for mobile technology and/or embedded systems,
   • algorithms of video compression and analysis through frequency or wavelet transforms or similar methods...

   After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

   Collaboration on grant projects, such as TACR, MPO, H2020, ECSEL (employment expected).

   Supervisor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

2. Automated Techniques for Requirements-Based Test Assessment

   Evaluation of the completeness of test suites with respect to software requirements represents a fundamental challenge in the verification of critical systems, particularly in regulated domains. Current practice is largely based on manual assessment, which is time-consuming and error-prone. This dissertation will focus on the research of formal and automated methods for analyzing requirement coverage by test cases, with an emphasis on the semantic aspects of requirements and their implicit behavior.

   The goal of the dissertation is to propose a formal framework for representing requirements and test cases, and to define objective coverage criteria derived from the semantics of requirements (e.g., combinations of conditions, temporal constraints, sequences of actions, or state transitions). On this basis, methods for computing the coverage level of a test suite and for identifying uncovered semantic cases will be investigated.

   The research will focus in particular on the following areas:

   • design and systematic comparison of formal models suitable for representing different classes of requirements and testing artifacts, including analysis of their expressive power and analyzability;
   • formalization of the notion of requirement coverage and derivation of corresponding coverage obligations from the formal semantics of requirements;
   • design of algorithms for analyzing test suite coverage over formal representations, including identification of uncovered cases and their linkage to specific parts of the requirements;
   • investigation of methods for transforming informal requirements and test cases into formal models, including the use of natural language processing techniques and large language models, with emphasis on the reliability and limitations of these approaches;
   • experimental evaluation of the proposed methods on realistic datasets, including assessment of accuracy, completeness, and scalability.

   The dissertation will also include the design and implementation of prototype tools supporting selected aspects of the proposed approach, along with their validation on examples from embedded and avionics systems. Emphasis will be placed on integrating formal methods, software testing, and automated analysis techniques, with the aim of contributing to systematic and repeatable evaluation of test suite quality.

   Supervisor: Rogalewicz Adam, doc. Mgr., Ph.D.

3. Detection and Prevention of Deepfake Attacks on Images of Human Faces

   The student will study techniques used in deepfake attacks on images and videos of human faces. The doctoral study is linked to the TrustedFace project (in collaboration with Innovatrics). The goal is to investigate existing deepfake attacks on images of human faces aimed at impersonating identities, quantify their severity, and propose possible defenses – both for detecting attacks and preventing them. The student will need to gain in-depth familiarity with available datasets of human faces, techniques for face identification and verification in images and video, and deepfake attack techniques. The ultimate goal of the study and doctoral work is to develop defenses against deepfake attacks and to formulate recommendations for systems that utilize face-based identification and verification.

   Supervisor: Herout Adam, prof. Ing., Ph.D.

4. Efficient Technology for Dealing with Logics and Automata (Not Only) for Formal Analysis and Verification

   Different types of logics and automata are among the most fundamental objects studied and applied in computer science for decades. Nevertheless, there are many unsatisfactorily solved problems in this area, and new, exciting problems related to ever new applications of logics and automata are constantly emerging (e.g., in the formal verification of finite and infinite state systems with various advanced control or data structures, in decision procedures, in program or hardware synthesis, in quantum program verification, or even in methods for efficient search in various types of data or network traffic).

   The subject of the dissertation will primarily be the development of the state of the art in efficient work with various logics (e.g. over pointer structures, strings, various arithmetic, temporal logics, etc.). To this end, approaches based on different types of decision diagrams, automata, but also e.g. approaches based on the existence of a model of bounded size or on efficient reductions between different types of logical theories will be investigated. In connection with this, methods for efficient work with decision diagrams and different types of automata (automata over words, trees, infinite words, automata with counters, registers, etc.) will be developed. The work will include theoretical research as well as prototype implementation of the proposed techniques and their experimental evaluation.

   The work will be solved in collaboration with the VeriFIT team working on the development of techniques for working with logics and automata and their applications at FIT BUT. There is also the possibility of close cooperation with various foreign partners of VeriFIT: Academia Sinica, Taiwan (Prof. Y. Chen); TU Vienna, Austria (Assoc. F. Zuleger); LSV, ENS Paris-Saclay (Assoc. M. Sighireanu); IRIF, Paris, France (Assoc. A. Bouajjani, Assoc. P. Habermehl, Assoc. C. Enea), Verimag, Grenoble, France (Assoc. R. Iosif); Uppsala University, Sweden (prof. P.A. Abdulla, prof. B. Jonsson); or RPTU, Kaiserslautern, Germany (prof. A.W. Lin).

   Supervisor: Lengál Ondřej, doc. Ing., Ph.D.

5. Embedded Systems for Video/Signal Processing

   The topic focuses embedded image, video and/or signal processing. Its main goal is to research capabilities of "smart" and "small" units that have such features that allow for their applications requiring smyll, hidden, distributed, low power, mechanically or climatically stressed systems suitable of processing of some signal input. Exploitation of such systems is perspective and wide and also client/server and/or cloud systems. The units themselves can be based on CPU/DSP/GPU/AI accelerator, programmable hardware, or their combination. Smart cameras can be considered as well. Applications of interest include:

   • classification of images or objects using machine learning (AI) using traditional methods or through deep convolution networks neural network or similar approaches (e.g. for industrial quality inspection, etc.),
   • parallel analysis of signal(s) and video (e.g. for robust detection of occurrence of object in industrial or surveillance applications),
   • modern algorithms of video, image, and/or signal exploiting "client/server" or "cloud" (with focus on the technlogy) suitable e.g. for mobile technology and/or embedded systems,
   • other similar topics can be individually consulted and considered.

   A possibility exists in collaboration on grant projects, especially the newly submitted TAČR, HE, ECSEL ones (employment expected).

   Supervisor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

6. Evaluating Vulnerabilities and Capabilities of Large Language Models

   Despite the impressive performance of large language models (LLMs), these models are still not well understood. A lot of effort is dedicated to evaluating the capabilities of LLMs and creating guardrails to prevent potentially harmful behaviours. However, small prompt variations still significantly alter the behaviour of LLMs, they often fail in tasks that are similar to the ones seen in training data but contain a small change, or they can be easily used to generate confident misinformation. As the large language models increasingly mediate access to information and are increasingly used by expert and non-expert users, it is essential to develop rigorous methods for evaluating their true capabilities and identifying the sources of their vulnerabilities.

   The goal of this research is to build a deeper and more reliable understanding of the LLM behaviour. The first possible research direction involves designing a benchmark that can accurately measure both advanced capabilities (such as reasoning, planning, understanding, math or multilingual capabilities) and common failure modes and vulnerabilities (including hallucination, brittleness, tendency or ease of generating problematic content such as misinformation). The second possible research direction leverages mechanistic interpretability to study the internal structures that drive the LLM behaviours. These tools will be used to investigate why specific vulnerabilities arise, how the capabilities arise, and whether targeted interventions can reduce failures while maintaining core capabilities.

   By integrating robust behavioural benchmarking with mechanistic insights, the project aims to produce a more principled understanding of how LLMs work and how they can be made safer and more reliable. The application domain includes (but are not limited to) low-resource tasks, multilingual understanding, misinformation and other problematic behaviour detection (narrative detection) or even LLM user simulation.

   Relevant publications:

   • Vykopal, I., Pikuliak, M., Srba, I., Moro, R., Macko, D., and Bielikova, M., 2023. 2024. Disinformation Capabilities of Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14830–14847, Bangkok, Thailand. Association for Computational Linguistics. https://aclanthology.org/2024.acl-long.793/
   • Zugecova, A., Macko, D., Srba, I., Moro, R., Kopal, J., Marcinčinová, K., and Mesarčík, M., 2025. Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 780–797, Vienna, Austria. Association for Computational Linguistics. https://aclanthology.org/2025.acl-long.38/

   The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with researchers from highly respected research units. A combined (external) form of study and full employment at KInIT is expected.

   Supervisor: Bieliková Mária, prof. Ing., Ph.D.

7. Formal Methods in Design and Analysis of Quantum Programs

   Development in the field of quantum computers is moving forward unstoppably. However, for the effective use of quantum hardware, it is necessary to have the right support at the software level, i.e., quantum algorithms for solving problems, as well as tools for compiling, optimizing and synthesizing quantum programs, mapping them to a specific quantum technology, mapping them to a fault tolerant protocol, analyzing, simulating, and debugging quantum programs, etc.

   The subject of the dissertation will primarily be the development of current knowledge in the field of formal methods in quantum program design. The main focus here will be on the use of automata theory, automatic reasoning, and decision diagrams. In connection with this, methods for effective work with decision diagrams and various types of automata (automata over words, trees, infinite words, automata with counters, registers, etc.) will also be developed. The work will include both theoretical research and prototype implementation of the proposed techniques and their experimental evaluation.

   The work will be carried out in collaboration with the VeriFIT team at FIT BUT, which develops techniques for working with logics and automata and their applications. There is also the possibility of close cooperation with various foreign VeriFIT partners: Academia Sinica, Taiwan (prof. Y. Chen); Uppsala University, Sweden (prof. P.A. Abdulla, prof. R. S. Thinniyam); or Leiden Institute for Advanced Computer Science, Leiden, the Netherlands (prof. A. Laarman).

   Supervisor: Lengál Ondřej, doc. Ing., Ph.D.

8. Homographic Navigation – augmented reality for capturing precise images of objects

   The aim of this work will be to develop the technique of "homographic navigation," a new concept being developed under the guidance of the doctoral program supervisor. The PhD student will develop computer vision algorithms and related machine learning techniques. Model training for homographic navigation must be minimally supervised. Both the learning algorithms and the inference and use in the application must be fast and resource-efficient. The project also involves prototyping and developing an augmented reality application that will be used for data collection, user testing, and other related tasks. Homographic navigation technology is intended to have immediate industrial applications, so the doctoral study will also include the development of all essential techniques through to the production phase.

   Supervisor: Herout Adam, prof. Ing., Ph.D.

9. Human in the loop (HIL) in the adaptation of AI systems

   This PhD topic is driven by the needs of users for which the privacy of data is crucial and concentrates on improving AI (especially speech recognition) system in case the user is able to identify and correct systems errors. The topic includes proper evaluations of HIL systems, selection of data to be proposed for correction and actual fine-tuning / adaptation techniques working with large pre-trained models. 

   Supervisor: Černocký Jan, prof. Dr. Ing.

10. Modern Algorithms of Computer Graphics

    The topic concerns algorithms of computer graphics and image synthesis. Its main goal is to research new algorithms so that their features and application possibilities are better understood so that they are improved or newly created. If suitable, it is possible to work on various platforms, includeing parallel CPUs, such as x86/64, ARM, Xeon PHI, GPU, etc. or other cores in CUDA, OpenCl, VHDL, etc. Algorithms of interest include:

    • rendering using selected computer graphics methods (such as ray tracing, photon mapping, direct rendering of "point clouds", etc.),
    • modeling of scenes and redering using artificial intelligence, including image synthesis using neural netowrks (especially CNNs),
    • processing and rendering of "lightfield" images, their acquisition, or possibly compression, reconstruction of 3D scenes from images and/or video, eventually also fusing with other sensors, such as LIDAR or RADAR,
    • modern algorithms of geometry suitable for applications in cpmputer graphics and perhaps also 3D printing,
    • emerging algorithms of 3D synthesis, holography, wavelet transform, frequency transform, etc.

    After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

    Collaboration on grant projects, such as TACR, HE, ECSEL (employment expected).

    Supervisor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

11. Non-Traditional Visal Computing Algorithms

    The topic of the work includes algorithms of comptuer graphics, image, and video, i.e. "Visual Computing", such as HDR (High Dynamic Range) image, multispectral image, stereoimage, possibly also image augmented with material features, temperature, etc. The goal is to understand the features and possibilites but also applications, to analyze the algorithms in-depth, prepare new and improve them. The possible algorithms include:
    
    

    • Acquisition of HDR image and video, its compression, assembling from several standard images, videos, etc.,
    • acquisition, processing, and visualization of mutispectral images (images with more than three spectral components),
    • processing of stereo images and other images containing the depth information using both structural light illumination or no structural light,
    • algorithms suitable for mobile technology and/or embedded systems focusing the above types of iamges, or images acquired from drones,
    • metods of visualization of HDR images and video, tone-mapping, real-time tone mapping,
    • algorithms and applications exploiting Wavelet, frequency, and/or other similar transformations.

     

    After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

    Collaboration on grant projects, such as TACR, MPO, HE, ECSEL (employment expected).

    Supervisor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

12. Performance of voice deepfake detection by humans and machines

    The work will start with getting familiar with the basics of the problem of voice deep fake detection (DFD), terminology, available techniques, data and challenges (AVSpoof, WildSpoof), with the history and state of the art techniques and tools for speaker recognition (wespeaker toolkit), with state of the art techniques and tools for personalized text to speech (pTTS) synthesis and voice conversion. The topic will then concentrate on the evaluation of human and machine performance in DFD, while concentrating on the motivation aspect of human subjects to simulate true attacks on people. The PhD will then progress in both (1) technical (advances in deepfake generation and detection technology) and (2) human-aspects. The work counts on collaboration with specialists from psychology and sociology. 

    Supervisor: Černocký Jan, prof. Dr. Ing.

13. Performance of voice deepfake detection by humans and machines

    The work will start with getting familiar with the basics of the problem of voice deep fake detection (DFD), terminology, available techniques, data and challenges (AVSpoof, WildSpoof), with the history and state of the art techniques and tools for speaker recognition (wespeaker toolkit), with state of the art techniques and tools for personalized text to speech (pTTS) synthesis and voice conversion. The topic will then concentrate on the evaluation of human and machine performance in DFD, while concentrating on the motivation aspect of human subjects to simulate true attacks on people. The PhD will then progress in both (1) technical (advances in deepfake generation and detection technology) and (2) human-aspects. The work counts on collaboration with specialists from psychology and sociology. 

    Supervisor: Černocký Jan, prof. Dr. Ing.

14. Speech recognition and data processing for ATC-pilot communication in aviation

    This PhD topic is focused on speech recognition and data processing for ATC-pilot communication in aviation. It will cover all components of automatic speech recognition (ASR), i.e. data processing, acoustic model, vocabulary (including special aviation terminology), language model as well as interaction with sources of meta-data (radar information, airport). Special attention will be paid to the use of large pre-trained model, code-switching, reliable language identification from short segments and adaptation to the conditions of given airport with crawled data.

    Supervisor: Černocký Jan, prof. Dr. Ing.

1. round (applications submitted from 01.03.2026 to 31.05.2026)

1. Advanced algorithms of Video, Image, and/or Signal processing

   The topic concerns algorithms of image, video, and/or signal processing. Its main goal is to research and in-depth analyze existing algorithms and discover new ones so that they have desirable features and so that they are possible to efficiently implement. Such efficient implementation can be but does not necessarily have to be part of the work but it is important to prepare the algorithms so that they can be efficiently implemented e.g. in CPU, in GPU, in AI accelratorrs, embeded systems, even in combination with FPGA, in extremely low power systems, or in other environments. It is possible to exploit algorithms of artificial intelligence, such as neural networks, especially CNNs, LLM/VLM. The application possibilities of the algorithms are also important and the application can be but does not have to be part of the work. The algorithms/applications of interest include:

   • recognition of scene contents, events, and general semantics of video sequences (such as identification of traffic situations, identification in scenes in moview, action identification, etc.),
   • classification of video sequences using machine learning (AI) through deep convolution networks neural network or similar approaches (e.g. for industrial quality inspection, object of scene characteristics search, etc.), possibly in combination with object tracking in video using modern methods,
   • parallel analysis of video and signal (e.g. for detection of coincidence of occurrence of object in video and characteristic signal shape in surveillance applications), fusion of video and sognals,
   • modern algorithms of video, image, and/or signal exploiting "client/server" or "cloud" approaches suitable e.g. for mobile technology and/or embedded systems,
   • algorithms of video compression and analysis through frequency or wavelet transforms or similar methods...

   After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

   Collaboration on grant projects, such as TACR, MPO, H2020, ECSEL (employment expected).

   Supervisor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

2. Automated Techniques for Requirements-Based Test Assessment

   Evaluation of the completeness of test suites with respect to software requirements represents a fundamental challenge in the verification of critical systems, particularly in regulated domains. Current practice is largely based on manual assessment, which is time-consuming and error-prone. This dissertation will focus on the research of formal and automated methods for analyzing requirement coverage by test cases, with an emphasis on the semantic aspects of requirements and their implicit behavior.

   The goal of the dissertation is to propose a formal framework for representing requirements and test cases, and to define objective coverage criteria derived from the semantics of requirements (e.g., combinations of conditions, temporal constraints, sequences of actions, or state transitions). On this basis, methods for computing the coverage level of a test suite and for identifying uncovered semantic cases will be investigated.

   The research will focus in particular on the following areas:

   • design and systematic comparison of formal models suitable for representing different classes of requirements and testing artifacts, including analysis of their expressive power and analyzability;
   • formalization of the notion of requirement coverage and derivation of corresponding coverage obligations from the formal semantics of requirements;
   • design of algorithms for analyzing test suite coverage over formal representations, including identification of uncovered cases and their linkage to specific parts of the requirements;
   • investigation of methods for transforming informal requirements and test cases into formal models, including the use of natural language processing techniques and large language models, with emphasis on the reliability and limitations of these approaches;
   • experimental evaluation of the proposed methods on realistic datasets, including assessment of accuracy, completeness, and scalability.

   The dissertation will also include the design and implementation of prototype tools supporting selected aspects of the proposed approach, along with their validation on examples from embedded and avionics systems. Emphasis will be placed on integrating formal methods, software testing, and automated analysis techniques, with the aim of contributing to systematic and repeatable evaluation of test suite quality.

   Supervisor: Rogalewicz Adam, doc. Mgr., Ph.D.

3. Detection and Prevention of Deepfake Attacks on Images of Human Faces

   The student will study techniques used in deepfake attacks on images and videos of human faces. The doctoral study is linked to the TrustedFace project (in collaboration with Innovatrics). The goal is to investigate existing deepfake attacks on images of human faces aimed at impersonating identities, quantify their severity, and propose possible defenses – both for detecting attacks and preventing them. The student will need to gain in-depth familiarity with available datasets of human faces, techniques for face identification and verification in images and video, and deepfake attack techniques. The ultimate goal of the study and doctoral work is to develop defenses against deepfake attacks and to formulate recommendations for systems that utilize face-based identification and verification.

   Supervisor: Herout Adam, prof. Ing., Ph.D.

4. Efficient Technology for Dealing with Logics and Automata (Not Only) for Formal Analysis and Verification

   Different types of logics and automata are among the most fundamental objects studied and applied in computer science for decades. Nevertheless, there are many unsatisfactorily solved problems in this area, and new, exciting problems related to ever new applications of logics and automata are constantly emerging (e.g., in the formal verification of finite and infinite state systems with various advanced control or data structures, in decision procedures, in program or hardware synthesis, in quantum program verification, or even in methods for efficient search in various types of data or network traffic).

   The subject of the dissertation will primarily be the development of the state of the art in efficient work with various logics (e.g. over pointer structures, strings, various arithmetic, temporal logics, etc.). To this end, approaches based on different types of decision diagrams, automata, but also e.g. approaches based on the existence of a model of bounded size or on efficient reductions between different types of logical theories will be investigated. In connection with this, methods for efficient work with decision diagrams and different types of automata (automata over words, trees, infinite words, automata with counters, registers, etc.) will be developed. The work will include theoretical research as well as prototype implementation of the proposed techniques and their experimental evaluation.

   The work will be solved in collaboration with the VeriFIT team working on the development of techniques for working with logics and automata and their applications at FIT BUT. There is also the possibility of close cooperation with various foreign partners of VeriFIT: Academia Sinica, Taiwan (Prof. Y. Chen); TU Vienna, Austria (Assoc. F. Zuleger); LSV, ENS Paris-Saclay (Assoc. M. Sighireanu); IRIF, Paris, France (Assoc. A. Bouajjani, Assoc. P. Habermehl, Assoc. C. Enea), Verimag, Grenoble, France (Assoc. R. Iosif); Uppsala University, Sweden (prof. P.A. Abdulla, prof. B. Jonsson); or RPTU, Kaiserslautern, Germany (prof. A.W. Lin).

   Supervisor: Lengál Ondřej, doc. Ing., Ph.D.

5. Embedded Systems for Video/Signal Processing

   The topic focuses embedded image, video and/or signal processing. Its main goal is to research capabilities of "smart" and "small" units that have such features that allow for their applications requiring smyll, hidden, distributed, low power, mechanically or climatically stressed systems suitable of processing of some signal input. Exploitation of such systems is perspective and wide and also client/server and/or cloud systems. The units themselves can be based on CPU/DSP/GPU/AI accelerator, programmable hardware, or their combination. Smart cameras can be considered as well. Applications of interest include:

   • classification of images or objects using machine learning (AI) using traditional methods or through deep convolution networks neural network or similar approaches (e.g. for industrial quality inspection, etc.),
   • parallel analysis of signal(s) and video (e.g. for robust detection of occurrence of object in industrial or surveillance applications),
   • modern algorithms of video, image, and/or signal exploiting "client/server" or "cloud" (with focus on the technlogy) suitable e.g. for mobile technology and/or embedded systems,
   • other similar topics can be individually consulted and considered.

   A possibility exists in collaboration on grant projects, especially the newly submitted TAČR, HE, ECSEL ones (employment expected).

   Supervisor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

6. Evaluating Vulnerabilities and Capabilities of Large Language Models

   Despite the impressive performance of large language models (LLMs), these models are still not well understood. A lot of effort is dedicated to evaluating the capabilities of LLMs and creating guardrails to prevent potentially harmful behaviours. However, small prompt variations still significantly alter the behaviour of LLMs, they often fail in tasks that are similar to the ones seen in training data but contain a small change, or they can be easily used to generate confident misinformation. As the large language models increasingly mediate access to information and are increasingly used by expert and non-expert users, it is essential to develop rigorous methods for evaluating their true capabilities and identifying the sources of their vulnerabilities.

   The goal of this research is to build a deeper and more reliable understanding of the LLM behaviour. The first possible research direction involves designing a benchmark that can accurately measure both advanced capabilities (such as reasoning, planning, understanding, math or multilingual capabilities) and common failure modes and vulnerabilities (including hallucination, brittleness, tendency or ease of generating problematic content such as misinformation). The second possible research direction leverages mechanistic interpretability to study the internal structures that drive the LLM behaviours. These tools will be used to investigate why specific vulnerabilities arise, how the capabilities arise, and whether targeted interventions can reduce failures while maintaining core capabilities.

   By integrating robust behavioural benchmarking with mechanistic insights, the project aims to produce a more principled understanding of how LLMs work and how they can be made safer and more reliable. The application domain includes (but are not limited to) low-resource tasks, multilingual understanding, misinformation and other problematic behaviour detection (narrative detection) or even LLM user simulation.

   Relevant publications:

   • Vykopal, I., Pikuliak, M., Srba, I., Moro, R., Macko, D., and Bielikova, M., 2023. 2024. Disinformation Capabilities of Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14830–14847, Bangkok, Thailand. Association for Computational Linguistics. https://aclanthology.org/2024.acl-long.793/
   • Zugecova, A., Macko, D., Srba, I., Moro, R., Kopal, J., Marcinčinová, K., and Mesarčík, M., 2025. Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 780–797, Vienna, Austria. Association for Computational Linguistics. https://aclanthology.org/2025.acl-long.38/

   The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with researchers from highly respected research units. A combined (external) form of study and full employment at KInIT is expected.

   Supervisor: Bieliková Mária, prof. Ing., Ph.D.

7. Formal Methods in Design and Analysis of Quantum Programs

   Development in the field of quantum computers is moving forward unstoppably. However, for the effective use of quantum hardware, it is necessary to have the right support at the software level, i.e., quantum algorithms for solving problems, as well as tools for compiling, optimizing and synthesizing quantum programs, mapping them to a specific quantum technology, mapping them to a fault tolerant protocol, analyzing, simulating, and debugging quantum programs, etc.

   The subject of the dissertation will primarily be the development of current knowledge in the field of formal methods in quantum program design. The main focus here will be on the use of automata theory, automatic reasoning, and decision diagrams. In connection with this, methods for effective work with decision diagrams and various types of automata (automata over words, trees, infinite words, automata with counters, registers, etc.) will also be developed. The work will include both theoretical research and prototype implementation of the proposed techniques and their experimental evaluation.

   The work will be carried out in collaboration with the VeriFIT team at FIT BUT, which develops techniques for working with logics and automata and their applications. There is also the possibility of close cooperation with various foreign VeriFIT partners: Academia Sinica, Taiwan (prof. Y. Chen); Uppsala University, Sweden (prof. P.A. Abdulla, prof. R. S. Thinniyam); or Leiden Institute for Advanced Computer Science, Leiden, the Netherlands (prof. A. Laarman).

   Supervisor: Lengál Ondřej, doc. Ing., Ph.D.

8. Homographic Navigation – augmented reality for capturing precise images of objects

   The aim of this work will be to develop the technique of "homographic navigation," a new concept being developed under the guidance of the doctoral program supervisor. The PhD student will develop computer vision algorithms and related machine learning techniques. Model training for homographic navigation must be minimally supervised. Both the learning algorithms and the inference and use in the application must be fast and resource-efficient. The project also involves prototyping and developing an augmented reality application that will be used for data collection, user testing, and other related tasks. Homographic navigation technology is intended to have immediate industrial applications, so the doctoral study will also include the development of all essential techniques through to the production phase.

   Supervisor: Herout Adam, prof. Ing., Ph.D.

9. Human in the loop (HIL) in the adaptation of AI systems

   This PhD topic is driven by the needs of users for which the privacy of data is crucial and concentrates on improving AI (especially speech recognition) system in case the user is able to identify and correct systems errors. The topic includes proper evaluations of HIL systems, selection of data to be proposed for correction and actual fine-tuning / adaptation techniques working with large pre-trained models. 

   Supervisor: Černocký Jan, prof. Dr. Ing.

10. Modern Algorithms of Computer Graphics

    The topic concerns algorithms of computer graphics and image synthesis. Its main goal is to research new algorithms so that their features and application possibilities are better understood so that they are improved or newly created. If suitable, it is possible to work on various platforms, includeing parallel CPUs, such as x86/64, ARM, Xeon PHI, GPU, etc. or other cores in CUDA, OpenCl, VHDL, etc. Algorithms of interest include:

    • rendering using selected computer graphics methods (such as ray tracing, photon mapping, direct rendering of "point clouds", etc.),
    • modeling of scenes and redering using artificial intelligence, including image synthesis using neural netowrks (especially CNNs),
    • processing and rendering of "lightfield" images, their acquisition, or possibly compression, reconstruction of 3D scenes from images and/or video, eventually also fusing with other sensors, such as LIDAR or RADAR,
    • modern algorithms of geometry suitable for applications in cpmputer graphics and perhaps also 3D printing,
    • emerging algorithms of 3D synthesis, holography, wavelet transform, frequency transform, etc.

    After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

    Collaboration on grant projects, such as TACR, HE, ECSEL (employment expected).

    Supervisor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

11. Non-Traditional Visal Computing Algorithms

    The topic of the work includes algorithms of comptuer graphics, image, and video, i.e. "Visual Computing", such as HDR (High Dynamic Range) image, multispectral image, stereoimage, possibly also image augmented with material features, temperature, etc. The goal is to understand the features and possibilites but also applications, to analyze the algorithms in-depth, prepare new and improve them. The possible algorithms include:
    
    

    • Acquisition of HDR image and video, its compression, assembling from several standard images, videos, etc.,
    • acquisition, processing, and visualization of mutispectral images (images with more than three spectral components),
    • processing of stereo images and other images containing the depth information using both structural light illumination or no structural light,
    • algorithms suitable for mobile technology and/or embedded systems focusing the above types of iamges, or images acquired from drones,
    • metods of visualization of HDR images and video, tone-mapping, real-time tone mapping,
    • algorithms and applications exploiting Wavelet, frequency, and/or other similar transformations.

     

    After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

    Collaboration on grant projects, such as TACR, MPO, HE, ECSEL (employment expected).

    Supervisor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

12. Performance of voice deepfake detection by humans and machines

    The work will start with getting familiar with the basics of the problem of voice deep fake detection (DFD), terminology, available techniques, data and challenges (AVSpoof, WildSpoof), with the history and state of the art techniques and tools for speaker recognition (wespeaker toolkit), with state of the art techniques and tools for personalized text to speech (pTTS) synthesis and voice conversion. The topic will then concentrate on the evaluation of human and machine performance in DFD, while concentrating on the motivation aspect of human subjects to simulate true attacks on people. The PhD will then progress in both (1) technical (advances in deepfake generation and detection technology) and (2) human-aspects. The work counts on collaboration with specialists from psychology and sociology. 

    Supervisor: Černocký Jan, prof. Dr. Ing.

13. Performance of voice deepfake detection by humans and machines

    The work will start with getting familiar with the basics of the problem of voice deep fake detection (DFD), terminology, available techniques, data and challenges (AVSpoof, WildSpoof), with the history and state of the art techniques and tools for speaker recognition (wespeaker toolkit), with state of the art techniques and tools for personalized text to speech (pTTS) synthesis and voice conversion. The topic will then concentrate on the evaluation of human and machine performance in DFD, while concentrating on the motivation aspect of human subjects to simulate true attacks on people. The PhD will then progress in both (1) technical (advances in deepfake generation and detection technology) and (2) human-aspects. The work counts on collaboration with specialists from psychology and sociology. 

    Supervisor: Černocký Jan, prof. Dr. Ing.

14. Speech recognition and data processing for ATC-pilot communication in aviation

    This PhD topic is focused on speech recognition and data processing for ATC-pilot communication in aviation. It will cover all components of automatic speech recognition (ASR), i.e. data processing, acoustic model, vocabulary (including special aviation terminology), language model as well as interaction with sources of meta-data (radar information, airport). Special attention will be paid to the use of large pre-trained model, code-switching, reliable language identification from short segments and adaptation to the conditions of given airport with crawled data.

    Supervisor: Černocký Jan, prof. Dr. Ing.