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 methods for MRI image analysis to improve image clarity and disease diagnosis

   Magnetic resonance imaging is nowadays becoming an increasingly accessible and progressive modality, often replacing previous diagnostic standards, and often becoming the first-choice examination. Consequently, the amount of data acquired by this modality is also increasing and thus the time requirements for its analysis by medical experts are higher. Supporting diagnostic tools then provide medical experts with an easier and more accurate view of the captured data, making it easier to work with it and offering the possibility of automated supporting diagnostics. Specifically, this can include the co-registration of contrast scans at different stages, automated segmentation of areas or pathologies, and their analysis in relation to the current diagnosis or prognosis. The topic focuses on the development, implementation and validation of advanced image processing techniques involving deep learning methods. The student will be work with data from MR modality, such as MRI of breast or brain tumors (gliomas), where the main task is to provide data pre-processing, extraction of parametric maps from multiphase or perfusion scans, analysis of the resulting parameters, and correct interpretation of the resulting relationships to the current diagnosis or prognosis. At the initial stage, however, this requires a thorough study of the issues, research and familiarization with the data and their pre-processing. As part of their studies, doctoral students complete several month-long internships at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or part-time in addition to the state stipend when participating in a grant project or participating in teaching.

   Tutor: Jakubíček Roman, Ing., Ph.D.

2. Advanced methods of medical image analysis in modern CT scanners

   The topic will aim to development of advanced image processing and analysis methods involving machine learning and deep learning approaches with a scope for multiparametric images acquired by multilayer CT detectors. The student will focus on the development, implementation and validation of preprocessing, segmentation, detection, classification, and prediction tasks considering the character of multiparametric images. The proposed complex computer-aided diagnostic tool will help increase diagnostic accuracy and reproducibility, speed of the examinations and decrease the inter-/intra-expert variability and routine workload. The topic will be solved at the Department of Biomedical Engineering. However, cooperation with our external partners is expected – national clinical institutions (FN Brno, VFN Prague, FNUSA/ICRC Brno) and foreign institutions (IRST IRCCS Meldola Italy, Philips Healthcare Netherlands, DKFZ Heidelberg Germany), allowing clinical evaluation of the results and their discussion with medical experts. As part of their studies, doctoral students complete several month-long internships at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or part-time in addition to the state stipend when participating in a grant project or participating in teaching.

   Tutor: Chmelík Jiří, Ing., Ph.D.

3. Analysis of movement stereotypes in sports performance

   Motion analysis in sports is irreplaceable. A detailed analysis of movement stereotypes leads to improved quality of training plans and improve sports results of training individuals. Analysis can also be used for diagnostic purposes - monitoring faulty movement patterns after injuries in order to clarify procedures for rehabilitation treatments. This work will be focused on monitoring specific movement stereotypes, selection of appropriate parameters and subsequent analysis of data, which will be performed in order to describe the motion stereotypes in sports performance. The work will be focused on the development of a methodology for tracking the movement of athletes. It will include the design of an optimal set of wireless sensors, its use and the development of motion analysis algorithms. The study will be carried out in cooperation with the Centre of Sports Activities of BUT. PhD students will complete a few-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: Kolářová Jana, doc. Ing., Ph.D.

4. Bioreactor optimization for cultivation of thermophiles

   White biotechnology, i.e. a technology that uses living cells to produce value added chemicals, usually loses the competition with standard petrochemical production due to higher financial costs. The reason can be found in the need to protect these processes against contamination. This inefficiency could be reduced by using naturally robust organisms, so called extremophiles. However, these organisms are not so well studied, partly also because of the lack of instrumentation for extremophilic cultivation on a small scale in laboratory bioreactors. The topic is focused on developing a small laboratory bioreactor especially suited for thermophilic cultivations. Large industrial processes usually generate waste heat that is unfavourable for mesophiles and needs to be reduced for them to proliferate. On the other hand, this environment is naturally suitable for extremophiles, particularly thermophiles. Unlike large scale processes, small scale lab cultivation does not produce waste heat, therefore, the heat has to be added for successful cultivation and research of thermophiles. Such experiments are needed to develop novel concepts as the Next-Generation Industrial Biotechnology concept that relies on the use of naturally robust organisms. Unfortunately, small bioreactors designed for thermophilic cultivations are currently missing. The aim of the research is to develop novel hardware for cultivations of bacterial thermophiles and its software control for various cultivation modes. A wide range of currently available parts will be used rather than building the reactor up from scratch. Platforms like Chi.Bio can be used as a base for it presents an open system orchestrated through Arduino and programmable in Python. Thus, it offers almost unlimited possibilities for bioreactor augmentation. The project will be solved mainly at the Department of Biomedical Engineering. However, cooperation with our national (University Hospital Brno, the Faculty of Chemistry BUT, and Czech Collection of Microorganisms) and foreign partners (Ludwig-Maximilians-Universität München in Germany and HES-SO Valais-Wallis in Switzerland) is expected. 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: Sedlář Karel, doc. Mgr. Ing., Ph.D.

5. Computational detection of small RNAs in bacterial genomes

   Thanks to their diversity, non-model bacteria represent an inexhaustible resource for microbial biotechnology. Their utilization is only limited by our lack of knowledge regarding the regulation of processes they are capable to perform. The particular problem lies in non-coding regulators, mainly small RNAs (sRNAs), that are not so widely studied as coding genes. During the last years, sRNAs were shown to play important regulatory roles in diverse cellular processes by participating in post-transcriptional regulation of gene expression. This is the reason why sRNAs are drawing more attention than ever before. The topic is focused on methods for computational inference of sRNA genes from bulk RNA-Seq datasets. While a plethora of specialized techniques for sRNA experimental identification exists, e.g., GRIL-Seq, RIP-Seq, RIL-Seq, their use in non-model bacteria is limited for their technical complexity. On the other hand, even standard RNA-Seq contains information on non-coding elements, including sRNAs, that can be mined by a computational approach. Unfortunately, current computational tools for sRNA inference does not match current standards in data processing. They require definition of threshold for detection that is dependent on sequencing depth and errors. Thus, they are not widely applicable for various sequencing platforms and libraries. The aim of the research is to find a method for sRNA inference without setting any additional parameters. Instead, the method will build upon thorough analysis of input dataset in order to make the detection independent of a sequencing library and platform. The applied method will include specific steps to process short NGS as well as long TGS reads to cover all currently used sequencing technologies. The project will be solved mainly at the Department of Biomedical Engineering. However, cooperation with our national (University Hospital Brno, the Faculty of Chemistry BUT, and Czech Collection of Microorganisms) and foreign partners (Ludwig-Maximilians-Universität München in Germany and HES-SO Valais-Wallis in Switzerland) is expected. 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: Sedlář Karel, doc. Mgr. Ing., Ph.D.

6. Deep learning methods for the analysis and identification of arrhythmogenic substrates in 3D electroanatomical imaging

   Atrial fibrillation is the result of structural changes in the myocardium and causes a number of complications, the most serious of which lead to the death of the patient. Invasive treatment uses 3D electroanatomical mapping methods as the gold standard for localization and subsequent ablation of the arrhythmogenic substrate. The complexity of the structural changes guarantees long-term success only in some patients. For this reason, it is necessary to identify new electroanatomical biomarkers related to arrhythmia recurrence, which would allow to increase the long-term effect of treatment. The topic is focused on the use of machine and deep learning to study structural and electrical changes in heart tissue in patients with atrial fibrillation. The topic aims to identify subjects who, in addition to the conventional method of pulmonary vein isolation, may require the modification of an additional arrhythmogenic substrate to increase the success of the therapy. The doctoral thesis is focused on the development, implementation and optimization of deep learning methods and related regularization techniques for deep regression analysis of the risk of recurrence of atrial fibrillation. The basis for processing are 3D spatial models of the left atrium and high-density time-space recordings of the local electrical activity of the heart tissue. The work envisages the development of unsupervised and weakly supervised learning methods for the optimization of regression models and proportional hazard models, including their calibration with respect to the observed cohort of patients and the conventional clinical scale of risk of recurrence of atrial fibrillation. The work will be carried out at the Institute of Biomedical Engineering in cooperation with the interventional cardiac electrophysiology research team FNUSA/ICRC Brno. The work is a continuation of the prospective multicenter study WaveMap, carried out in cooperation with the company Abbott Laboratories (USA). As part of their studies, doctoral students complete six-month internships at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or part-time in addition to the state stipend when participating in a grant project or participating in teaching.

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

7. Detection and characterization of pulmonary nodules in lung cancer low-dose CT screening

   The topic is focused on methods for detection and characterization of pulmonary nodules in lung cancer low-dose CT screening image datasets. All data are available at cooperating institution Masaryk Memorial Cancer Institute in Brno where the screening programme is running since September 2022. The basic task for the topic is detection of nodules in lung cancer with various techniques of image processing. This part is well described in literature and several studies are published each year. Nodules are in form of opacities in lung parenchyma with no relation to a normal anatomy. Second task is nodule characterization based on classification on two main criteria – size and type. The aim of characterization is to find predictors of tumours malignancy. The results will be compared with an expert in radiology field and also with commercially available CAD system for CT scans evaluation. As part of their studies, doctoral students complete several month-long internships at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or part-time in addition to the state stipend when participating in a grant project or participating in teaching.

   Tutor: Mézl Martin, Ing., Ph.D.

8. Machine-Learning Reconstruction of MRI Sequences for Perfusion Imaging

   The project is focused on designing novel deep-learning methods for reconstructing high-temporal-resolution MR image sequences from perfusion MRI data acquired under conditions that necessitate correction of artifacts, such as subject motion, eddy currents, or thermal noise. The project will involve utilizing and potentially expanding upon the existing perfusometric data-acquisition simulator, PerfSim, as well as leveraging and potentially enhancing verified perfusion-analysis methods implemented in the PerfLab software. Both resources are available at the Institute of Scientific Instruments, Czech Academy of Sciences (ISI). The work will be carried out in close collaboration with the team of Olivier Keunen at the Luxembourg Institute of Health (LIH). Evaluation of the proposed methods will be conducted using real datasets available at LIH and ISI, and possibly other datasets provided by our collaborators.

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

9. Movement analysis of patients with neurological problems

   Patients with neurological problems have problems with movement. Brain, spinal cord and nerves are effected mostly. There are often disorders of motor control and movement coordination. Common movement problems include tremors, slowing of movement, muscle stiffness, limitation of range of motion, and other symptoms. Movement analysis in these patients can be a tool for the objective assessment of neurological problems, can predict the onset of problems, specify a specific dysfunction or be used for an individual therapeutic approach. The work will be focused on the development of a methodology for monitoring the movement of patients with neurological problems. It will include the design of an optimal set of wireless sensors, its use and the development of motion analysis algorithms. This work will be solved in cooperation with the CEITEC MUNI. As part of their studies, doctoral students complete several month-long internships at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or part-time in addition to the state stipend when participating in a grant project or participating in teaching.

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

10. New approaches in computational analyses of bacterial communities for biotechnology

    Thanks to their diversity, non-model bacteria represent an inexhaustible resource for microbial biotechnology. While tools, including the computational ones, to study pure bacterial cultures are developed to at least a certain point, their counterparts for analysis of mixed cultures are underdeveloped or completely missing. This prevent us to further study biotechnological capacity of bacterial consortia to produce value added chemicals. The topic is focused on computational methods for a comprehensive analysis of microbial consortia in order to reveal their functional capacity for industrial biotechnology and production of value added chemicals, primarily bioplastics. While particular tools for taxonomic profiling based on amplicon sequencing and metagenome analysis based on shotgun sequencing exist, they are oriented to perform descriptive rather than functional analysis. This provides only limited use for biotechnology research where the emphasis is put on function. This is partly caused also by the lack of tools oriented on processing of bacterial metatranscriptomes. Finally, there is an absolute lack of tools to connect potential functional capacity inferred from a metagenome with running biological processes measured with metatranscriptomics and metabonomics approaches. The aim of the research is to set up comprehensive computational pipeline to analyse diversity of a selected mixed bacterial culture, to set up a metagenome of this community, and to match its observed behaviour through analyses of other omics data revealing running biological and metabolic processes. The pipeline will include specific steps to process short NGS as well as long TGS reads to cover all currently used sequencing technologies. The project will be solved mainly at the Department of Biomedical Engineering. However, cooperation with our national (University Hospital Brno, the Faculty of Chemistry BUT, and Czech Collection of Microorganisms) and foreign partners (Ludwig-Maximilians-Universität München in Germany and HES-SO Valais-Wallis in Switzerland) is expected. 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: Sedlář Karel, doc. Mgr. Ing., Ph.D.

11. New approaches in recruiting bacterial genomes from metagenomes

    Recent advances in DNA sequencing allowed routine sequencing of environmental samples. However, current computational tools hardly keep up with constantly changing lab techniques and the growing output of sequencing devices. Therefore, novel computationally efficient techniques are needed to recruit particular genomes from metagenomes. The topic is focused on methods for recruiting particular bacterial genomes from environmental samples, i.e., metagenomes. While in the past all newly described bacteria had to be isolated and their culture had to be made publicly available, a recent initiative SeqCode brought a nomenclatural code for prokaryotes described directly from sequence data as many microbial species are uncultivable with current techniques. Moreover, even for newly published cultured bacteria, environmental evidence based on searching in publicly available metagenomes is nowadays required by scientific journals. Although tools to produce metagenome-assembled genomes exist, searching metagenomes for particular analysed genomes is done exclusively with BLAST and is not rigorously described. Unfortunately, due to the repetitive segments of bacterial genomes, false hits are always found and quantification of data, i.e., assuming an abundance of a genome in a metagenome, is therefore biased. The aim of the research is to find a method for quantification as precise as possible. The applied method will include specific steps to process short NGS as well as long TGS reads to cover all currently used sequencing technologies. The project will be solved mainly at the Department of Biomedical Engineering. However, cooperation with our national (University Hospital Brno, the Faculty of Chemistry BUT, and Czech Collection of Microorganisms) and foreign partners (Ludwig-Maximilians-Universität München in Germany and HES-SO Valais-Wallis in Switzerland) is expected. 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. 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: Sedlář Karel, doc. Mgr. Ing., Ph.D.

12. New methods for studying skin layers in drug application and tissue regeneration

    Examination and accurate qualification of skin and subcutaneous layers during their regeneration is a very topical biomedical problem. This problem is also related to the qualification of tissue properties at different depths for the analysis of stability or degradation of transplants and implants in regenerative medicine. The research topic focuses on the development and testing of a methodology for trans endodermal delivery of different types of drugs (labelled nanoparticles, liposomes, exosomes or other particles) through model and real skin layers. The Ph.D. student will primarily conduct research in the biology laboratory and will focus on the establishment of model skin epithelial cultures and the application of laboratory procedures, trans-epithelial/endothelial electrical resistivity methods, and confocal fluorescence microscopy techniques to test the model layer and analyze drug transfer across the model layer in in-vivo experiments. It will also involve the use of assistive techniques (e.g. optical coherence tomography) to test transfers to real skin in in-vivo experiments in animal models. The PhD student will be involved in interdisciplinary research in a project that involves working with endothelial and epithelial cells, creating cell monolayers and multilayers including model layer testing, testing and application of advanced drugs, and using advanced instrumentation and methodologies to acquire and interpret imaging data and further analyze it. The topic is provided in collaboration with the Research Facility of the The Veterinary Research Institute (VRI). As part of their studies, doctoral students complete several month-long internships at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or part-time in addition to the state stipend when participating in a grant project or participating in teaching.

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