Full-time study

4 years

prof. Ing. Valentýna Provazník, Ph.D.

Chairman :
prof. Ing. Valentýna 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. José Millet Roig
prof. Mgr. Jiří Damborský, Dr.
prof. MUDr. Marie Nováková, Ph.D.
prof. Ewaryst Tkacz, Ph.D.,D.Sc.
prof. Pharm.Dr. Petr Babula, 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. 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.

   Supervisor: 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.

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

3. 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. 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.

   Supervisor: Čmiel Vratislav, Ing., Ph.D., MBA

4. 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.

   Supervisor: Jan Jiří, prof. Ing., CSc.

5. 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.

   Supervisor: Provazník Valentýna, prof. Ing., Ph.D.

6. 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.

   Supervisor: Provazník Valentýna, prof. Ing., Ph.D.