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 approches to detect and predict of horizontal gene transfer between bacteria

   The PhD topic will be focused on the 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 consider high-throughput shotgun data analysis from animal farms. Other sequencing technologies and strategies (e.g. Oxford Nanopore Sequencing, plasmidome sequencing, functional metagenomics) 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 dissemination of drug resistance. 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: Čejková Darina, Mgr. Bc., Ph.D.

2. 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. 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: Smital Lukáš, Ing., Ph.D.

3. 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 tumours (gliomas), where the main task is to provide data pre-processing capabilities, 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 familiarisation with the data and their pre-processing. The student will be a valuable member of a stable research group, which has long been cooperating with several national medical institutions (FNUSA, ICRC Brno, FN Brno, VFN Prague) including foreign ones such as IRST IRCCS Meldola Italy, King' College London or Philips Healthcare, Netherlands. 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: Jakubíček Roman, Ing., Ph.D.

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

   Computed tomography scanners are one of the most used modalities for diagnosing various diseases and pathologies. Nowadays, the development and clinical use of modern CT scanners enabling multi-energy X-ray imaging using multilayer detectors or even single photon level imaging are taking place. At the same time, the devices provide a range of parametric images, such as monoenergetic images, material decomposition images, etc. It appears that this information increases the diagnostic yield of CT imaging modalities, with a significant dose reduction, which is in the interest of the wide medical community. 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. 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: Chmelík Jiří, Ing., Ph.D.

5. Detection and characterization of lung 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. 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. For testing purposes, the available dataset will be used and also additional data will be available at cooperating institution Masaryk Memorial Cancer Institute in Brno where the screening programme is running since September 2022. 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: Mézl Martin, Ing., Ph.D.

6. Methods and materials for 3D bioprinting of blood vessels

   Bioprinting in 3D is an advanced manufacturing technique capable of producing tissue-shaped constructs. A range of hydrogel bioinks was introduced to design these structures; however, there is a limitation in available bioinks that can mimic the vascular composition of native tissues. Current bioinks lack high printability and are unable to deposit a high density of living cells into complex 3D architectures, making them less effective. The work is focused on the research of new approaches in design of a 3D-bioprinted model of a blood vessel that mimics its behavior in living organism. 3D-bioprinted vessels provide a tool to understand vascular disease pathophysiology and assess therapeutics in preclinical trials. Bioprinting in 3D is an advanced manufacturing technique capable of producing tissue-shaped constructs in a layer-by-layer fashion with embedded living cells, making the arrangement to mirror multicellular makeup of vascular structures. There is a limitation in avalable hydrogel bioinks that can mimic the vascular composition of native tissues. Current bioinks lack high printability and are unable to deposit a high density of living cells into complex 3D tissue architectures. The main aim of the project is to develop a new nanoengineered bioink to print anatomically accurate multicellular blood vessels. The nanoengineered bioink will be printed into 3D cylindrical blood vessels consisting of living co-cultures of endothelial cells and vascular smooth muscle cells. The final construct must provide the opportunity to model vascular function and disease impact. The project require design and characterization of appropriate nanomaterials to develop a new bioink. 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: Provazník Valentine, prof. Ing., Ph.D.

7. Modern image processing methods in cardiac MRI applications

   Nuclear magnetic resonance imaging is one of the most advanced imaging systems in medicine. The development of these methods and the improved availability of these systems brings additional areas in which these methods can be used for diagnosis. This brings with it much larger volumes of data acquired by this modality and the resulting need for new methods that will allow for the processing of these data while providing more advanced and accurate diagnostics. One of these areas is cardiac MRI, which is the topic of this dissertation. The very first step is the correct orientation of the heart, i.e. finding the radiological planes that are important for the correct imaging of the heart using nuclear magnetic resonance. Here it is shown that the use of machine learning based methods (deep learning) could enable automatic detection from the survey data and thus can both speed up the scanning process and make it more accurate. The next step is to design appropriate methods to support the diagnosis of heart disease. These include both segmentation methods that can lead to a more detailed analysis of the heart (cardiac volumes, myocardial thickness, etc.) and other advanced methods based on deep learning to support diagnosis (detection of tissue changes, lesions, anatomical differences, etc.). However, cooperation with external partners - national clinical centres (FN Brno, VFN Prague, FNUSA/ICRC Brno) and foreign institutions (IRST IRCCS Meldola Italy, Philips Healthcare Netherlands, DKFZ Heidelberg Germany) is envisaged, enabling clinical evaluation of results and their discussion with expert physicians. 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.

8. 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.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 methodology for quantification as precise as possible. The applied methodology 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.

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 this 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. The applied methodology will include specific image processing to extract new spatial maps related to blood volume changes, extraction of specific temporal signals from video data and application of appropriate methods to reveal the relation between physiological signals and retinal image data. 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. 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: Kolář Radim, doc. Ing., Ph.D.

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

    This research topic focuses on the development and testing methodology of trans endodermal drug delivery of different types of drugs (labelled nanoparticles, liposomes, exosomes or others) across model and real skin layers. The PhD student will primarily perform research in the biology lab and will target the creation of model skin epithelial cultures and apply laboratory procedures, trans-epithelial/endothelial electrical resistance method, and confocal fluorescence microscopy techniques to test the model layer and analyse drug transfers through the model layer in in-vivo experiments. It will also include the use of supporting techniques (e.g., optical coherence tomography) for testing transfers to real skin in in-vivo experiments on animal models. The PhD student will participate in interdisciplinary research in the project, which covers experimental work with endothelial and epithelial cells, create cell mono and multiple layers including testing of model layers, testing, and applying modern drugs, using advanced instrumentation and methodology for image data acquisition and interpretation and further analysis. 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: Kolář Radim, doc. Ing., Ph.D.

11. Signal processing techniques for digital drawing tests

    Drawing tests are a common tool in psychological evaluations as they provide insight into an individual's development. These tests have several advantages for patients but can be difficult for evaluators to interpret and are subject to personal bias. The digitization of drawing tests together with the subsequent development of signal processing techniques will bring objectivity to the assessment process and create new psychometric markers for reliable diagnosis. The topic is focused on creating innovative signal processing methods for automatic analysis of the drawing process. The PhD student's goals will be to investigate the characteristics of drawing data and create quantifiers to describe time characteristic and distortion level of digital drawing strokes that will facilitate the automatic evaluation of drawing process. PhD student will assess the effectiveness and suitability of developed methods for use in the pediatric population. The PhD student will be involved in interdisciplinary research carried out mainly at the Department of Biomedical Engineering in collaboration with the psychologist and digital drawing acquisition experts, but international cooperation as well as internships at partner universities abroad is expected. The drawing data acquisition concept has already been designed and validated, and pilot experiments have been completed with data available for analysis. As a result, the student will be able to smoothly integrate into the research with the goal of identifying potentially significant psychometric markers that can be used to diagnose specific diseases. 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: Janoušek Oto, Ing., Ph.D.

12. Study of antibacterial nanomaterials for implant coating applications

    Tissues in the periphery of an implant material are vulnerable to infections and occurrence of such infections can lead to the failure of the implant and the surgery. The use of antibiotics is a common practice in such cases. But, bacterial infection may form a biofilm on the surface of the implant material and eventually reduce or can completely inhibit the antibacterial efficiency of the bactericidal drugs. Thus, the addition of antibacterial coatings onto the surface of the implants can resolve the issue of the development of such infections. By virtue of their high surface area to volume ratio, nanomaterials have higher antibacterial properties in comparison to the traditional antibacterial counterparts. These nanomaterials can provide more active area for biological interactions to occur and are likely to have exceptional research values in implant coating applications. The aim of the project is to prepare such nanomaterials, characterize them to know their morphologies, chemical compositions as well as their stability and thereafter study their antibacterial properties and biocompatibility. It will be an interdisciplinary research project where the doctoral student will acquire the experience of preparing nanomaterials mainly through chemical techniques and characterize them using electron microscopy, X-ray diffractometry & different spectroscopic analyses. The student will get exposure to experimentally investigate the compatibility of the synthesized nanomaterials to different cell lines, explore their antibacterial properties and eventually prepare nanocoatings for implants. The project will be carried out mainly at the Department of Biomedical Engineering, BUT, however, collaboration with our partner organizations is also 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: Paul Rima, Dr.

13. 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 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 01.04.2023 to 30.04.2023)

1. Advanced algorithms for health monitoring using smart devices

   The topic of this Ph.D. thesis is focused on monitoring of human health using smart devices (e.g. smartphone, smartwatch, smartband). Smart devices have various integrated sensors which potential is not fully exploited. Especially from PPG, many health features (such as heart rate, breathing rate, blood oxygen saturation, glycemia) can be computed. Smart devices can currently detect atrial fibrillation from ECG or PPG signals. However, it is possible to detect more pathologies. Practical objective of this Ph.D. thesis is to develop advanced algorithms for extraction of health features and to evaluate the performance and applicability of these algorithms in practice. It will be necessary to develop reliable PPG waves detector. It is possible to use some publicly available datasets with annotations and it is possible to create own database with use of smart devices available at DBME. 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: Němcová Andrea, Ing., Ph.D.

2. Artificial intelligence methods in perfusion imaging

   The topic of this dissertation is focused on design of new advanced methods of perfusion imaging based on artificial intelligence, mostly on deep learning. The project will include studying of actual approaches to reconstruction of perfusometric image data, calculation of concentration curves and their fitting with pharmacokinetic models of the second generation. Furthermore, the project will cover design of new deep learning procedures for the selected parts of the perfusion-analysis processing chain, or for the complete processing task. The design will be based on the current simulator of MR perfusion data acquisition, its appropriate extensions, available real datasets and on standard validated perfusion-analysis methods of the software PerfLab, available at ISI CAS. Evaluation of the proposed methods will be carried out on available real datasets. The work will be done in cooperation with the team of Luxembourg Institute of Health.

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

3. Health and activity monitoring using smart/experimental devices

   Commercial smart devices (e.g. smartphone, smartwatch, smartring) are widespread in the world population and their development is very fast. Nowadays, some experimental devices are developed and tested (e.g. electronical tatoos, bioimpedance sensors). The main objective of this Ph.D. thesis is to develop robust, fast and efficient algorithms for health and activity monitoring using smart devices. Development of cuff-less non-invasive blood pressure estimation algorithm is highly desired because of its potential to reveal high blood pressure in general public. Heart rate variability is another important health feature which can be monitored using smart devices. It brings important information about health and is useful for athletes to monitor their performance and recovery. The aforementioned sensors and features can help to detect or even predict cardiovascular diseases. Fusion of health features with activity features brings comprehensive view on the monitored subject. There exist datasets suitable for development of such algorithms, however it is expected to create own database using commercial and experimental smart devices available at DBME and it is possible to develop own experimental device. 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: Kolářová Jana, doc. Ing., Ph.D.