Full-time study

4 years

doc. Ing. Radim Kolář, Ph.D.

Chairman :
doc. Ing. Radim Kolář, Ph.D.
Councillor internal :
doc. Ing. Jana Kolářová, Ph.D.
doc. Ing. Daniel Schwarz, Ph.D.
prof. Ing. Valentýna Provazník, 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 genetic variant analysis using multi-omics data

   The project aims to develop advanced bioinformatics methods for integrating genomic and transcriptomic data to improve diagnostics and advance personalized medicine. By developing novel algorithms, leveraging in silico modeling, and applying machine learning, the project seeks to enhance the interpretation of genetic variants. The key objectives are: 1. Develop bioinformatics algorithms to facilitate the transition from panel sequencing to whole-exome sequencing (WES) and integrate these data efficiently. 2. Improve the interpretation of variants of uncertain significance (VUS) by combining genomic and transcriptomic data. The project will utilize both panel sequencing and WES to analyze genetic variants across diverse patient samples, identifying potential pathogenic variants contributing to genetically driven diseases. Additionally, transcriptomic analysis will be conducted using bulk RNA-Seq, incorporating advanced annotation and prioritization tools to assess the biological impact of genetic variants. The project will be carried out in collaboration with CIIRC CTU, the First Faculty of Medicine of Charles University, and University Hospital Ostrava. 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 Valentýna, prof. Ing., Ph.D.

2. Advanced methods for MRI image analysis to increase diagnostic yield

   AI-assisted analysis of 3D MR data for accurate diagnosis is increasingly replacing traditional diagnostic methods, leading to an increase in imaging data and higher demands on expert analysis. This research focuses on the development and validation of deep learning-based complex tools for automated MR data processing and analysis. Key areas include registration, automatic segmentation of pathologies, and characteristic analysis for diagnosis and prognosis. Current applications focus on MR breast, perfusion brain scans and cardiac examination with an emphasis on data preprocessing, parametric map and characteristics extraction and their clinical interpretation. The student will be a valid member of a BioImage_BUT research team that collaborates with leading national (FNUSA Brno, FNB Brno, VFN Prague) and international medical institutions (UMC Amsterdam, KCL London, DKFZ Germany, REUH Riga).

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

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

   Computed tomography scanners are 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. This information appears to increase 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 at the development of advanced image processing and analysis methods involving machine learning and deep learning approaches with 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.

   Tutor: Chmelík Jiří, Ing., Ph.D.

4. Advancing the applications of imaging methods in ophthalmology with adaptive optics imaging

   Over the past decade, advancements in retinal imaging - particularly through adaptive optics - have transformed our ability to achieve cellular-level resolution. This breakthrough enables in-vivo analysis of previously uncharted retinal structures, driving the need for an innovative and robust image analysis pipeline.

   This PhD project focuses on the detailed analysis of retinal vascularity and photoreceptors, incorporating techniques such as segmentation, pathology detection, multimodal image registration, and image quality assessment. It also offers opportunities to explore novel research areas, such as the effects of pregnancy on vascularity, potential pathological changes, and the impact of emerging drug treatments on retinal health.

   The project will be conducted at the Department of Biomedical Engineering, with anticipated collaboration with international partners, including Leipzig University and the University of Halle.

   Your task:

   • Development of an innovative image analysis pipeline for adaptive optics retinal images.
   • Multidimensional statistical analysis of various features measured under different conditions.
   • Multimodal image analysis of correlation across the retinal imaging datasets.
   • Scientific publishing.

   Requirements:

   • Master's degree in a relevant field, e.g. computer science, biomedical engineering.
   • Strong programming skills, preferably Python, MATLAB, or C++.
   • Familiarity with image processing techniques and machine learning algorithms.
   • Good communication skills in English.
   • A keen interest in ophthalmological research.

   We offer:

   • Collaboration within an active and interdisciplinary team and with international experts.
   • Friendly and supportive research environment.
   • Opportunities for professional development, including participation in international conferences, workshops, and specialized training.

   Relevant publications:

   https://doi.org/10.1364/BOE.471881

   https://doi.org/10.1016/j.preghy.2023.12.004

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

5. Applications of chromatic pupillometry in vision research and neurology

   Pupillometry has emerged as a powerful, non-invasive tool for assessing visual and neurological function. In particular, chromatic pupillometry, which utilizes different wavelengths of light to stimulate specific retinal and neural pathways, holds significant potential for advancing diagnostics in ophthalmology and neurology.

   This PhD project will focus on the development and application of a chromatic pupillometer, enabling precise assessment of pupil responses under controlled chromatic stimuli. The core focus of this PhD project is the development of a chromatic pupillometer, designed for both clinical and research applications. As part of this work, the candidate will actively participate in data acquisition, collecting pupillary response data from both healthy individuals and patient cohorts to investigate dynamic changes in pupil behaviour. Additionally, the project involves developing advanced data processing pipelines to analyse pupillary responses under various chromatic conditions. A key objective is to identify potential biomarkers that could aid in the diagnosis and monitoring of neurological and ophthalmic disorders based on pupillary behaviour.

   The project will be conducted at the Department of Biomedical Engineering, with anticipated collaboration with CEITEC MU and St. Anne’s University Hospital Brno.

   Your tasks:

   • Development of chromatic pupillometer, protocol design, data acquisition, interaction with ophthalmologists.
   • Signal and data analysis, biomarker identification.
   • Implementation of chromatic pupillometry to MR acquisition.

   Requirements:

   • Master's degree in a relevant field, e.g. computer science, biomedical engineering.
   • Programming skills, preferably Python, MATLAB, or C++.
   • Familiarity with image processing techniques, experiences with basic electronics or machine vision setups.
   • Good communication skills in English.
   • A keen interest in ophthalmological research.

   We offer:

   • Collaboration within an active and highly interdisciplinary team.
   • Friendly and supportive research environment.
   • Opportunities for professional development, including participation in international conferences, workshops, and specialized training.

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

6. Bioreactor optimization for cultivation of extremophiles

   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.

7. Computational Characterization of Enzymes for Sustainable Design of Bioplastics

   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 biological production of plastics is not an exception, mainly because of insufficient characterization of enzymes responsible for synthesis of various polymers. Although these enzymes are quite abundant in bacteria, systematic computational research based on analysis of their sequences has not been performed so far.

   The topic is focused on developing a computational pipeline for analysis of sequences of polyhydroxyalkanoate (PHA) synthases with the ultimate goal of creating their comprehensive database. PHA are microbial polyesters synthesized by various prokaryotic microorganisms with great potential for plastics industry. However, their wider use is still limited by a lack of fundamental knowledge on key genes/enzymes in various prokaryotes responsible for their synthesis, preventing the use of the most suitable organisms and their potential genetic engineering necessary to establish economically feasible processes. The aim of the research is to analyse all currently available genome sequences in order to annotate PHA synthases and classify them into four known classes or to propose their novel classification. Additionally, particular classes will be characterized by matching sequences with physicochemical properties of polymers they synthesize. Proposed computational pipelines for analysis of close and distant orthologues will be deployed together with a database of PHA synthases.

   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.

8. Determination of stress zones during sports performance using wearable electronics

   Determining physiological thresholds and correctly defining training zones are crucial for optimizing athletic performance and preventing overtraining. Thanks to advancements in wearable devices and heart rate variability (HRV) analysis methods, new opportunities are emerging for non-invasive and automated evaluation of these thresholds. This research will focus on the application of advanced machine learning methods, particularly neural networks, for the prediction of the aerobic (HRVT1) and anaerobic thresholds (HRVT2) using HRV analysis. The study will include a comparison of various HRV parameters and their suitability for accurate determination of training zones.

   An essential part of the work will be analyzing the accuracy of these methods under laboratory conditions as well as their applicability in field measurements. Furthermore, the issue of artifact detection and correction in HRV signals — which can significantly affect results, especially during physical activities — will be addressed. The implemented neural models will be tested and validated on an extensive database of real-world sports data. The outcomes of this research may contribute to more accurate individualized training plans and can be utilized to improve sports performance monitoring in practice.

   The practical part of the work will be carried out in collaboration with the Center for Sports Activities at Brno University of Technology (BUT), where experiments and verification of the proposed methods' accuracy in real-life conditions will take place.

   As part of their studies, PhD students will also participate in several-month internships at prestigious partner universities abroad. The Department of Biomedical Engineering (UBMI) provides PhD students with scholarships and/or part-time employment in addition to the standard state scholarship, particularly when involved in grant projects or teaching activities.

   Tutor: Smital Lukáš, Ing., Ph.D.

9. Determination of sudden cardiac death risk

   The implantable cardioverter defibrillator (ICD) is the most widely used therapeutic strategy for sudden cardiac death (SCD) prevention. The challenge is that there is currently no reliable method for accurately identifying at-risk patients. Current procedures are predominantly based on left ventricular ejection fraction and NYHA functional class assessment. However, many patients selected using these criteria do not benefit from ICDs and are exposed to risks, including complications during ICD implantation and potentially unnecessary discharges. Previous studies have proposed a wide range of predictors for sudden cardiac death risk, such as late potentials, heart rate variability, periodic repolarization dynamics, baroreflex sensitivity, and long QT interval syndrome. Despite promising findings from initial research, none of these predictors have been widely adopted in clinical practice. Ultra-high-frequency ECG (UHF ECG) is an innovative technology that analyzes frequency bands ranging from 150 to 1000 Hz. UHF ECG enables the monitoring of temporal differences in the depolarization of various heart segments. This year, a new method called broad-band ECG (BBECG) was introduced, which combines information from both low- and high-frequency bands. This approach provides improved monitoring of cardiac segments distant from the chest surface. Currently, both methods rely on frequency band averaging, leaving potentially valuable information in individual frequency bands unanalyzed. This research will focus on identifying new predictors of sudden cardiac death risk using techniques such as UHF ECG and BBECG. The most significant predictors will be integrated into an AI-based classification model. Data are already available from 265 patients with ICDs who are being followed up long-term. Among these, the ICD has successfully prevented life-threatening episodes in 78 patients. Additionally, public databases will also be utilized for this research. This topic is part of an international project currently under evaluation for funding within the Marie Skłodowska-Curie Actions Doctoral Networks. The research will be carried out in close collaboration with partners from this project, including research and commercial institutions from nine European countries. The findings of this research have the potential to significantly enhance patient stratification for ICD implantation, minimizing unnecessary procedures and improving patient outcomes.

   Tutor: Smíšek Radovan, Ing., Ph.D.

10. Electroactive hydrogels for biomedical applications

    Piezoelectric materials are smart materials that can generate electrical activity in response to minute deformations. For biomedical applications, piezoelectric materials allow for the delivery of an electrical stimulus without the need for an external power source. As a scaffold for tissue engineering, there is growing interest in piezoelectric materials due to their potential of providing electrical stimulation to cells to promote tissue formation. The project focuses on the fabrication of fibers from piezoelectric material and electroconductive polymer, attractive materials for making functional scaffolds, via electro-spin coating method. Electrospun scaffolds can produce electrical charges during mechanical deformation, which can provide necessary stimulation for cardiac tissue. The candidate will work on the fabrication of scaffolds with randomly oriented or uniaxially aligned fibers. The scaffolds will be characterized using various methods such as SEM, XPS, FTIR, XRD, contact angle. The next part of the project will focus on their potential contribution in the use of these piezoactive materials in hydrogel structures. In addition, the biological characterizations of scaffolds including viability assays and the detection of parameters demonstrating electromechanical activation of cells will be performed.

    Tutor: Fohlerová Zdenka, doc. Mgr., Ph.D.

11. Methodological and Empirical Strategies for LAG3-Targeted Immunotherapy in Oncology

    Lymphocyte Activation Gene-3 (LAG3; CD223) represents a promising target for cancer immunotherapy, given its function as a negative regulator of T cells and its ability, when paired with PD1, to induce a state of exhaustion. The impetus for investigating LAG-3 as a protein target in cancer immunotherapy arises from its significant function in immune regulation, its synergistic interactions with other immune checkpoints, and its binding affinity to various ligands, including MHC Class II, FGL1, Galectin-3, and LSECtin. The advancement of LAG-3 targeted immunotherapies in oncology depends on both computational and experimental methodologies to discern, refine, and authenticate potential therapeutic candidates. Investigations into the structural dynamics of LAG-3 interactions with its ligands, including MHC class II and FGL1, have elucidated the mechanisms underlying binding processes. These investigations inform the systematic development of small molecules or antibodies that interfere with these interactions. The processes of pre-clinical validation, structural validation, and approaches centered on combination therapies facilitate the development of more effective treatments customized to the unique profiles of individual patients.

    The applicant possesses an extensive background of collaboration with various national medical institutions, such as Mendel University, FNUSA, and ICRC Brno. Furthermore, he is collaborating with international partners located in Germany, the United Kingdom, and India, each of whom possesses specialized expertise and will contribute to different phases of the project's execution.

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

12. Methods for analysis of low-dose CT images

    The topic focuses on the processing of imaging data from low-dose CT scans, which are used in screening programs, for example, for the early detection of lung cancer. During the course of this project, methods will be designed and implemented to enhance the utility of data obtained from these examinations. The primary aim is the detection of lung nodules and their subsequent classification based on size, shape, and other characteristics. The topic will be addressed using available datasets from international institutions, and also implementation for data from the Masaryk Memorial Cancer Institute in Brno and the General University Hospital (VFN) in Prague — where screening studies have been conducted for several years—will also be processed. The project will further expand to include clinical data from other areas, as the number of low-dose CT scans is expected to rise not only in screening but also in other medical fields.

    Tutor: Mézl Martin, Ing., Ph.D.

13. 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.

14. 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 or their bioremediation potential.

    The topic is focused on computational methods for a comprehensive analysis of microbial consortia in order to reveal their functional capacity for industrial biotechnology, bioremediation, 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.

15. New computational approaches in recruiting bacterial genomes from metagenomes

    Recent advances in sequencing technologies have enabled routine sequencing of metagenomic samples from various environments, significantly expanding our ability to identify and analyze bacterial species within these systems. In the past, all newly described bacteria had to be isolated and their cultures made publicly available, which posed a significant challenge since many microbial species are uncultivable using current techniques. However, this requirement has been changed by the SeqCode initiative, which introduced a nomenclatural code allowing the description of prokaryotes directly from sequencing data, thereby greatly expanding the possibilities for their classification and study. To confirm their existence, computational methods such as bacterial recruitment are used, enabling the detection of specific bacteria in metagenomic databases. However, there is currently no standardized methodology for this technique, and commonly used approaches, often relying on BLAST, may lead to false-positive results due to shared genetic segments among different species. Therefore, this research aims to find a method for quantification as precise as possible. The methodology will involve processing both short NGS and long TGS sequencing reads to cover all currently used sequencing technologies. The proposed method could contribute to the more efficient detection of novel microorganisms and help to understand better their role in clinical and environmental metagenomes. The project will be primarily carried out at the Department of Biomedical Engineering, with expected collaboration with the Center for Molecular Biology and Genetics, FN Brno, Mendel University in Brno and Faculty of Pharmacy, Masaryk University. PhD students will complete six-month internships at prestigious partner universities abroad as part of their studies. DBME 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: Jakubíčková Markéta, Ing., Ph.D.

16. Organs-on-a-chip for drug screening, environmental pollution assessment, and nanoplastics toxicity evaluation.

    The absence of predictive platforms that accurately mimic human physiology highlights the need for more physiologically relevant models. Such models enable a comprehensive and systematic assessment of the toxic properties of micro- and nanoparticles from environmental pollutants (e.g. metal nanoparticles, plastics) or facilitate the screening of newly developed drugs. Advances in microfabrication techniques have enhanced the ability of organ-on-a-chip (OOC) models to replicate vivo-like microenvironments and physiological responses, offering a promising avenue for nanotoxicological research. The work will include design of OOC leading to the improvement its accuracy by eliminating unspecific nanomaterial/drug adsorption while maintaining cellular function. Developing body-on-a-chip model that consists of multiple engineered tissues to analyse potential indirect effects of nanoparticles/drugs. The quantify the overall tissue in real-time fashion will enhance the utility of organ-on-a-chip system (integration of electronic components enabling the analysis of biological molecules and detection of cellular functional changes).

    Tutor: Fohlerová Zdenka, doc. Mgr., Ph.D.

17. RAGE for Multiple Diseases: A Repurposing Drug Approach Using Artificial Intelligence and Systems Biology

    The Receptor for Advanced Glycation End Products (RAGE) is a crucial target in the treatment of several diseases, as it is associated with numerous inflammatory and degenerative conditions. This project will utilize advanced artificial intelligence (AI) and systems biology approaches to explore the potential for drug repurposing targeting RAGE. The impetus for my current research arises from various factors. RAGE is associated with various clinical conditions, including inflammatory illnesses, diabetes, Alzheimer's disease, cardiovascular diseases, and cancer. Repurposing existing pharmaceuticals can significantly reduce the duration and cost of drug research, hence accelerating the introduction of innovative therapies for patients. Recent breakthroughs in AI and systems biology facilitate the prediction of drug-target interactions and the examination of complex biological systems. Innovative therapeutic approaches are urgently required, as numerous RAGE-associated illnesses lack viable treatments. The group has a lengthy history of working with a number of national medical institutes, including Mendel University, FNUSA, and ICRC Brno. Additionally, we have foreign partners in Germany, the UK, and India that specialize in certain areas and will be involved in various stages of the project's completion.

    Tutor: Roy Sudeep, Ph.D.

18. Real-time identification of pathogenic bacteria during nanopore sequencing

    Recent advances in third-generation sequencing technologies have enabled routine DNA sequencing of microbial samples in clinical practice. This greatly increases our ability to identify and analyze dangerous bacterial species and allows a more effective approach preventing their spread in the human population. Although the whole-genome sequencing is becoming a leading technique in clinical microbiology, its full-scale deployment is still limited by the high time and computational demands of sequencing data processing. Analysis of sequencing data still takes from tens of hours, for individual samples, to days and weeks for massive deployment of parallelized sequencing of large numbers of samples. The most time-consuming phase of this process is basecalling, i.e. decoding DNA from the "raw" signals. For nanopore sequencing, this phase starts during the sequencing run and for the high-precision models required for clinical diagnostics, it continues for days after the sequencing run is complete. The topic of this dissertation is focused on designing a new method based on machine learning techniques to identify features of bacterial resistance and virulence directly from raw signals without the need to decode the DNA sequence. The advantage of this approach is that complete genetic information of the bacteria is not needed to identify these features, only the partial information available during the first hours of the sequencing run is sufficient. Thus, identification of potential epidemiological risks can be achieved before the sequencing run is finished. The project will be primarily carried out at the Department of Biomedical Engineering, with expected collaboration with the Center for Molecular Biology and Genetics, FN Brno, and Mendel University in Brno. PhD students will complete six-month internships at prestigious partner universities abroad as part of their studies. DBME 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.

19. Study of piezoelectric biomaterial-based scaffolds for bone tissue engineering

    Signal conduction stiffness in scaffold-based tissue engineering therapies interferes with normal signaling pathways despite having their therapeutic advantages. Consequently, physiological applications necessitate smart biomaterials that produce and convey bioelectric signals akin to biological tissues. Piezoelectric biomaterials respond to moderate mechanical stress by generating electrical impulses that activate signaling pathways and facilitate tissue repair. Illness or injury can cause bone abnormalities. The conventional site-specific critical degeneration treatment is detrimental. Notwithstanding the existing advantages of tissue engineering, its application is constrained by cellular and growth factor therapies. Consequently, a potential alternative to the repair and regeneration of hard tissue is necessary. Engineered piezoelectric tissue analogs can reinstate cellular functioning. The project focuses on developing, characterizing, and conducting the required multidimensional analyses for bone tissue engineering using scaffolds made of piezoelectric biomaterials. It will be an interdisciplinary research project where the doctoral student will acquire the experience of preparing piezoelectric biomaterial through chemical techniques, fabricating scaffolds using a 3D bioprinter, and characterizing them using electron microscopy, X-ray diffractometry & different spectroscopic analyses. The student will get exposure to experimentally investigate the biocompatibility of the prepared materials, explore their piezoelectric properties, and eventually make in-vitro & in-vivo studies for bone regeneration. The project will primarily be carried out in the Department of Biomedical Engineering, however, collaboration with our partner universities and organizations is anticipated.

    Tutor: Paul Rima, Dr.