Full-time study

4 years

prof. Ing. Valentine Provazník, Ph.D.

Chairman :
prof. Ing. Valentine Provazník, Ph.D.
Councillor internal :
doc. Ing. Radim Kolář, Ph.D.
doc. Ing. Jana Kolářová, Ph.D.
doc. Ing. Daniel Schwarz, Ph.D.
Councillor external :
prof. Mgr. Jiří Damborský, Dr.
prof. Pharm.Dr. Petr Babula, Ph.D.
Prof. José Millet Roig
prof. Ewaryst Tkacz, Ph.D.,D.Sc.
prof. MUDr. Marie Nováková, Ph.D.
prof. Dr. Marcin Grzegorzek

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 mainly 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 the full-time students submit the elaborated dissertation thesis to the supervisor, who scores this elaborate. The combined students submit the elaborated dissertation thesis by the end of winter term in the fifth year of study. The final dissertation thesis is expected to be submitted by the student by the end of the fourth or fifth year of the full-time or combined study form, respectively.
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. Advanced algorithms for monitoring human health and activity using a smartphone

   The theme of this dissertation is focused on monitoring of human health and activity using a smartphone and its integrated sensors (especially accelerometer, gyroscope, magnetometer, GPS, microphone, camera). The main motivation is the availability and great potential of smartphones, which is far from being used in health monitoring. The thesis has two main objectives. The first objective is to explore the potential of a smartphone and how to use it for human health and activity monitoring and to critically evaluate its real usability. The second objective of the thesis is to design advanced algorithms for processing of data captured by a smartphone (e.g. for the purpose of human activity classification or blood pressure determination) and to evaluate the performance and applicability of these algorithms in practice. It is possible to use smartphones available at the department to record own data. Applicants are expected to have knowledge of programming in Matlab or Python and base knowledge of processing and analysis of 1D signals. PhD students will complete a six-month internships at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or a part-time contract beyond the state stipend when joining a grant project or engaging in teaching.

   Tutor: Vítek Martin, Ing., Ph.D.

2. Advanced detection of ECG significant points during pathological events

   The theme of this dissertation is focused on reliable and accurate detection of ECG significant points during pathological events. The thesis has two main objectives. The first objective is to map the potential of nowadays algorithms for QRS complex detection and ECG records delineation during various pathological events and to define their deficiencies. The second goal of the thesis is to design an advanced delineation algorithm that will work reliably during most common pathological events and verify its robustness on standard ECG databases. Applicants are expected to have knowledge of programming in Matlab or Python and base knowledge of processing and analysis of 1D signals. PhD students will complete a six-month internships at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or a part-time contract beyond the state stipend when joining a grant project or engaging in teaching.

   Tutor: Vítek Martin, Ing., Ph.D.

3. Advanced methods for biological signals quality estimation

   The topic of dissertation thesis is focused on biological signals quality monitoring by wearable devices (e.g. PPG, ECG). Other concurrently sensed signals such as accelerometer data can be also used for this purpose. The thesis has two main objectives. The first objective is to propose signal quality classes with respect to possible sources of interference as well as the subsequent utilization of the signal. The second objective is to design advanced algorithms for real-time signal quality estimation and to verify the usability of the signal class for its intended purpose. Applicants are expected to have knowledge of programming in Matlab or Python and base knowledge of processing and analysis of 1D signals. It is possible to use wearable devices available at the department to record own data. The Department provides doctoral students with a scholarship beyond the state scholarship in the form of a supplementary stipend or salary when participating in a grant project. PhD students will complete a six-month internship at attractive partner universities abroad.

   Tutor: Smital Lukáš, Ing., Ph.D.

4. Advanced methods for study of mitochondrial function and activity with application in regenerative medicine

   In most cells, mitochondria are the main producers of cellular energy with oxygen consumption and are also involved in apoptotic and other intracellular regulatory processes. They also play an important role in the field of regenerative medicine, where studies demonstrating mitochondrial transfer as one of the emerging mechanisms by which mesenchymal stem cells can regenerate and repair damaged cells or tissues. Microscopic fluorescence techniques are often used to real-time monitoring of mitochondrial function, particularly using fluorescent dyes based on mitochondrial membrane potential measurements. However, the reproducibility of the results across laboratories strongly depends upon following well validated and reliable protocols along with the appropriate controls. The dissertation thesis will deal with the research and development of new methods for studying cell function and activity using advanced fluorescent techniques, using fluorescent and fluorescent confocal microscopy and optical spectroscopy. The aim of this work is to create a methodology for evaluation of mitochondrial function and activity using in regenerative medicine in order to evaluate efficiency and rate of regeneration. Part of this work will be the creation of methodology for controlled simulation of mitochondrial function and activity in controlled environment. The Department provides doctoral students with a scholarship beyond the state scholarship in the form of a supplementary stipend or salary when participating in a grant project. PhD students will complete a six-month internship at attractive partner universities abroad.

   Tutor: Čmiel Vratislav, Ing., Ph.D.

5. Arrhythmia detection and classification in ambulatory electrocardiograms using deep learning

   Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow. Widely available digital ECG data and paradigms of artificial intelligence present an opportunity to substantially improve the accuracy of automated ECG analysis. Advanced AI methods, such as deep-learning convolutional neural networks, enable rapid, human-like interpretation of the ECG, while signals and patterns largely unrecognizable to human interpreters can be detected by multilayer AI networks with precision, making the ECG a powerful, non-invasive biomarker. The main aim of the project is to demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. The developed algorithms for computerized ECG interpretations will improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent patterns. The Department provides doctoral students with a scholarship beyond the state scholarship in the form of a supplementary stipend or salary when participating in a grant project. PhD students will complete a six-month internship at attractive partner universities abroad.

   Tutor: Provazník Valentine, prof. Ing., Ph.D.

6. Computerized fetal heart rate analysis

   The topic of the study is focused on the analysis of fetal heart rate (CTG, cardiotachogram) in order to monitor CTG changes in fetuses with premature amniotic fluid outflow during pregnancy. During the doctoral study, the student will get acquainted with the methods of CTG analysis and CTG variability in order to determine the current state of the fetus. CTG signals obtained from two groups of pregnant women (with premature amniotic fluid outflow and normal pregnancy) using advanced methods will be analyzed and compared with results obtained by physicians using currently generally accepted CTG assessment criteria. The data analysis will take place in cooperation with the medical team of the maternity ward of the Brno University Hospital. The candidate is expected to have knowledge of programming in Matlab or Python and an overview of the processing and analysis of biological signals. As part of their studies, doctoral students complete six-month internships at attractive partner universities abroad. UBMI provides doctoral students with scholarships and / or part-time work beyond the state scholarship when participating in a grant project or participating in teaching.

   Tutor: Kolářová Jana, doc. Ing., Ph.D.

7. Detection and prediction of horizontal gene transfer between bacteria within and between different microbiomes

   The microbiome is the collection of microorganisms (bacteria) and their genetic material within an environment, e. g. human, animal, river microbiome. It is a dynamic system which is shaped by ability to transfer genetic material between different bacterial species. So-called horizontal gene transfer mediated by mobile elements plays a central role in the evolution of many environments and their bacterial members. It is very common in human pathogenic bacteria; thus the process contributes to increased pathogenesis and virulence, and also to resistance to antibiotic treatment. Furthermore, the resistance traits may be also transferred from pathogenic to helpful and commensal bacteria in the environment and between different environments. Resistance genes have recently been disseminated globally to every environment, including drinking water systems. The PhD topic will be focused on identification and characterization of mobile genetic material (transposons, plasmids, antibiotic resistance genes) from complex ecosystems but also from individually sequenced microbiota member and to determine the bacterial reservoirs of such genes and traits. The bioinformatic approaches will mostly consider high-throughput shotgun data analysis from different environments, such as hospital and municipal waste-water treatment plants, municipal landfill, animal farms or wildlife, i.e., in the one health settings. Other sequencing technologies and strategies (e.g. Oxford Nanopore Sequencing, plasmidome sequencing) will be used and analyzed as well. In parallel, novel computational methods will be designed to examine to which extent closely related species share horizontally acquired genes and to distinguish those genes from phylogenetically shared genes. The outcome of the project will lay foundation to track and link the reservoirs and horizontal transfer of antibiotic resistance genes, with the ultimate goal of slowing down the spread of drug resistance. PhD students will complete a six-month internship at attractive partner universities abroad. UBMI provides doctoral students with a stipend and a part-time contract.

   Tutor: Čejková Darina, Mgr. Bc., Ph.D.

8. Medical image segmentation using deep learning techniques

   The theme of this thesis is aimed on medical image segmentation and classification using deep learning methods. The first aim of this thesis is to improve on actual methods for segmentation of 2D medical images. In next step these methods will be adapted for segmentation of 3D volume images, especially images from microCT system. The classification of images using deep learning methods will be also part of this thesis. Machine learning methods, especially neural networks, which represents new and perspective algorithms of image processing, will be used for the solution of this thesis. The main aim of this thesis is extend possibilities of automatic processing and classification of large volume of data like images from CT scanners. PhD students will complete a six-month internship at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or a part-time contract beyond the state stipend when joining a grant project or engaging in teaching.

   Tutor: Harabiš Vratislav, Ing., Ph.D.

9. New imaging and image processing approaches for retinal diseases monitoring

   The topic is focused on methods for simultaneous evaluation of retinal oxygenation and blood circulation including development of a specific ophthalmic device and appropriate image processing methods. The basic concept of this ophthalmic device has been already designed and verified during last 3 years. The modifications of this concept will enable to capture retinal videosequences at multiple wavelengths and simultaneous acquisition of various biosignals – mainly electrocardiogram, photoplethysmographic and respiratory signal. The doctoral student will thus participate in an interdisciplinary research in the frame of larger project, which covers areas such as retinal imaging and its functional evaluation, as well as advanced image and signal processing and machine learning. The aim of the research is to find a methodology for the evaluation of retinal oxygenation, including potentially important biomarkers suitable for the diagnosis of specific diseases. Project will be solved mainly at the Department of Biomedical Engineering. However, cooperation with our foreign partners is expected - Leipzig University and Friedrich-Alexander-Universität Erlangen-Nürnberg in Germany and University of Minnesota, USA. The Department provides doctoral students with a scholarship beyond the state scholarship in the form of a supplementary stipend or salary when participating in a grant project. PhD students will complete a six-month internship at attractive partner universities abroad.

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

10. Utilization of signal processing techniques for refinement of nanopore sequencing data decoding

    The aim of the dissertation is to develop methodology for pre-processing of raw nanopore sequencing data consisting from signal reads called “squiggles”. The proposed procedure should precede DNA sequence decoding, where the neural networks are used exclusively nowadays. The decoding step called “basecalling” is the main source of errors in nanopore sequencing data processing. Although the nowadays basecalling methods for nanopore sequencing have significantly increased accuracy in the last years, it still can fall to 95 % and that is insufficient for clinical utilization. Appropriate combination of advance signal filtering of high level noise, signal segmentation into specific sections called “events” corresponding to DNA symbols and mutual adjustment of events durations by dynamic time warping can significantly improve accuracy of genetic information decoding. The applicant is expected being mastering basic methodology of processing and analysis of genomic data and should also have an overview in the field of processing and analysis of 1D signals. The programming in appropriate environment is commonplace. The topic will be solved in cooperation with the Children's Hospital - University Hospital Brno. PhD students will complete a six-month internship at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or a part-time contract beyond the state stipend when joining a grant project or engaging in teaching.

    Tutor: Vítková Helena, Ing., Ph.D.

1. round (applications submitted from 01.04.2021 to 15.05.2021)

1. Advanced algorithms for ECG analysis

   The dissertation topic is aimed on design and development of new sophisticated methods for ECG analysis in order to improve the ECG-based detection of cardiac arrhythmias. It will be mainly focused on the analysis of standard surface ECG and intracardial data recorded simultaneously during the catheter ablation procedure. Implemented algorithms will improve the parametrization of ECGs with manifestation of poorly recognizable atrial arrhytmias, such as fibrillation, flutter, tachycardia, etc. The topic implies the use of advanced machine learning methods, including state-of-the-art deep learning approaches yielding excellent results in biomedical data processing and analysis. To develop and test the algorithms, data from the Children’s Hospital and the Internal cardiology clinic (University Hospital Brno) will be used. The topic will be a follow-up to the conducting DBME research. The applicant is expected to have knowledge of programming in Python and knowledge of biological signal processing and analysis as well as recent machine learning approaches. PhD students will complete a six-month internship at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or a part-time contract beyond the state stipend when joining a grant project or engaging in teaching.

   Tutor: Filipenská Marina, Ing., Ph.D.

2. Advanced methods for analysis of bacterial methylomes on a genome-wide scale using nanopore sequencing

   Changes in the expression of genetic information that are not caused by changes in the primary structure of DNA are referred to as epigenetic changes. A typical example can be found in DNA methylation, which was discovered in bacteria more than a half century ago. Despite that, a majority of studies aims on 5-methylcytosine (5mC) methylations in eukaryotic genomes by utilizing bisulfite sequencing with the next generation sequencing platforms. Unfortunately, bacterial methylomes are formed not only by 5mC, but also by N6-methyladenine (6mA) and N4-methylcytosine (4mC) methylations, which are undetectable (6mA) or difficult to detect (4mC) by this approach. The topic is focused on the solution existing in the utilization of the third generation sequencing (TGS) platforms. Although the nanopore TGS sequencing allows theoretically the detection of all above-mentioned types of methylations, this potential remains currently unused due to the lack of bioinformatics tools for the detection of methylated nucleotides in the current signal that is produced during data acquisition. The aim of this dissertation is to create a methodology for the detection of methylations using advanced bioinformatics and digital signal processing techniques for filtering and analyzing this noisy current signal, referred to as squiggle. The whole methodology will be designed using data produced by Oxford Nanopore Technologies MinION and MinION/Flongle sequencing devices that UBMI owns. Suitable bacterial strains will be provided by cooperating institutions, mainly University Hospital Brno (pathogenic bacteria), UCT Prague, and the Faculty of Chemistry BUT (industrially utilizable bacteria). Project will be solved mainly at the Department of Biomedical Engineering. However, cooperation with our foreign partners is expected - Ludwig-Maximilians-Universität München in Germany and HES-SO Valais-Wallis in Switzerland. The Department provides doctoral students with a scholarship beyond the state scholarship in the form of a supplementary stipend or salary when participating in a grant project. PhD students will complete a six-month internship at attractive partner universities abroad.

   Tutor: Sedlář Karel, doc. Mgr. Ing., Ph.D.

3. Analysis of lesion temporal development in medical image data

   Evaluation of structural, intensity and shape changes of treated cancer lesions is crucial for the diagnosis and treatment planning. Nowadays, the 3D tomographic imaging is important basis for diagnosis and prognosis of the oncological disease and for its effective treatment. Supporting the medical staff in these actions by computer methods requires applying advanced image data analysis enabling detection, segmentation, feature extraction and classification of ill-defined lesions and their temporal development. This way provided modelling of the illness development may allow more reliable diagnostic conclusions, efficient treatment design and patient recovery predictions. However, the respective computational methodology requires substantial further research. The topic concerns analysis of temporal development of tumorous lesions during the disease treatment. The analysis of the respective tomographic CT and MRI image data will require modifying or completely developing advanced 3D image processing methods, suitable for application on the medical image data. Besides classical methods, modern machine deep learning approaches will be used and the results compared, also with respect to justifying the respective medical conclusions. In parallel, the patient-specific models should be designed and tested thus aiming at following and predicting the illness progress. It is expected that the dissertation project will be run at the Department of Biomedical Engineering, FEEC, Brno University of Technology, in cooperation with medical partners, namely the University Hospital Brno and General University Hospital in Prague. The Department provides doctoral students with a scholarship beyond the state scholarship in the form of a supplementary stipend or salary when participating in a grant project. PhD students will complete a six-month internship at attractive partner universities abroad.

   Tutor: Jan Jiří, prof. Ing., CSc.

4. Deep learning as a computational modelling technique for genomics

   As a data-driven science, genomics utilizes machine learning to search for dependencies in data and hypothesize novel biological phenomena. The need for extraction of new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. Deep learning is becoming the method of choice for many genomics modelling tasks suche as predicting the impact of genetic variation on gene regulatory mechanisms. The main aim of the project is to design novel tools for genomic data partitioning and prediction, fitting parameters and choosing hyperparameters for optimal training of deep neural networks. The tools will be used to discover local patterns and longe-range dependencies in sequential data and modelling transcription factor binding sites and spacing. The Department provides doctoral students with a scholarship beyond the state scholarship in the form of a supplementary stipend or salary when participating in a grant project. PhD students will complete a six-month internship at attractive partner universities abroad.

   Tutor: Provazník Valentine, prof. Ing., Ph.D.

5. Genetic variation in cardiomyopathy and coronary artery disease

   Patients suffering cardiovascular diseases such as cardiomyopathy and coronary artery disease tend to cluster in families due to underlying monogenic or polygenic genetic architectures. The main aim of the project is search for genetic variation in these diseases in order to find causative genes and susceptibility loci. Distribution of the allele frequencies of the selected set of loci in a sample population will be analyzed and modelled. The study will be extended to identify loci that implicate pathways in blood vessel morphogenesis and inflammation related to the diseases. Data from 1000 Genomes Project and from CARDIoGRAMplusC4D Consortium project will be used to conduct large genome-wide bioinformatics analysis. There will opportunities to develop and apply research methodologies in statistical genetics and bioinformatics, develop skills in programming in high-level analysis packages, and develop skills in high-performance computing. The Department provides doctoral students with a scholarship beyond the state scholarship in the form of a supplementary stipend or salary when participating in a grant project. PhD students will complete a six-month internship at attractive partner universities abroad.

   Tutor: Provazník Valentine, prof. Ing., Ph.D.