Combined 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 signal processing methods for invasive EEG from macro and micro electrodes

   Invasive EEG (iEEG) is recorded in patients with drug-resistant, focal epilepsy during their pre-surgical evaluation. The current gold standard, defined 70 years ago, requires visual evaluation, which is extremely time consuming and unreliable. With modern technology and computational power available today, a significant part of manual evaluation might be replaced by fast, automatic and objective models. Improved and more effective pre-surgical evaluation can significantly reduce risks, time of diagnostic evaluation and costs, as well as increase post-surgical success rates and patients’ overall well-being. The aim of this project is development of advanced signal processing and machine learning methods for analytical evaluation of iEEG in order to study new biomarkers of epilepsy, spectral properties of epileptogenic zones and connectivity patterns in micro and macro domain. This project represents an unprecedented opportunity to study a unique and extensive dataset of iEEG recorded by standard clinical macro electrodes and experimental micro electrodes with high sampling rates up to 32 kHz. These recordings are provided by three different international institutions (St. Anne’s University Hospital Brno, Montreal Neurological Institute, and Mayo Clinic).

   Tutor: Jurák Pavel, Ing., CSc.

2. 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.

1. round (applications submitted from 01.04.2021 to 15.05.2021)

1. CT data based thrombi detection and classifiction in patients with acute ischemic stroke

   Stroke is one of the leading cause of death and is a major cause of serious disability for adults. The patient acute treatment choice - intravenous thrombolysis or mechanical thrombectomy is based on clinical and anamnestic data together with imaging techniques, of which the computed tomography (CT) is the primary choice for acute ischemic stroke diagnosis. The treatment selection and its outcome can be affected by many factors and one of them is the thrombus composition. Better characterization of thrombus based on analysis of CT data can lead to more specific treatment choices (e.g. different thromboembolic strategy) and thus to better clinical outcome for the patient. The topic concerns detection, segmentation and classification of thrombus in native CT (nCT) and multiphase CT angiography (CTA) data. Thrombi can be characterized a.o. by their permeability as measured by changes of densities in the CT images. The problem encompasses 3D registration of the CT data, localization od the thrombi and local properties analysis of the imaged tissues enabling the segmentation and classification of thrombi. Pseudo-perfusion calculation in selected occluded artery/thrombus will be necessary to calculate the perfusion parameters. The relevant parameters evaluation will be performed in comparison with characteristics of thromboembolic samples. The work will utilize the experience of the image processing team at the UBMI institute and follow up with solving the mentioned specific problem. The expected outcome of the dissertation project is the respective algorithm with methodologically original elements that will be publishable in a high quality impacted journal. The dissertation project will be realised at the Clinics of Radiology St. Anne's University Hospital Brno, in frame of the doctoral study at the Department of Biomedical Engineering (UBMI), FEEC, Brno University of Technology.

   Tutor: Jan Jiří, prof. Ing., CSc.

2. 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.

3. 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.

4. Quantitative MRI methods in cardiology

   The aim of the PhD project is to design and validate new MRI methods in cardiology for quantitative assessment of selected pathologies. Examples of quantitative MRI are quantification of T1, DCE-MRI and ASL perfusometry. These are experimental methods in preclinical and clinical research. The project should contribute to increased accuracy of the given biomarkers' estimates with the aim at better applicability in practice. In frame of the project, new acquisition methods and the subsequent methods for estimation of parametric maps will be developed with regard to the specifics of cardiac imaging. Emphasis wil be put on retrospective synchronization with cardiac and respiratory activity, furthermore, on use of compressed sensing, possibly deep learning. The project will be mostly run on the preclinical MR scanner available at ISI CAS.

   Tutor: Jiřík Radovan, doc. Ing., Ph.D.