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Original title in Czech: Biomedicínské technologie a bioinformatikaFaculty: FEECAbbreviation: DPC-BTBAcad. year: 2026/2027
Type of study programme: Doctoral
Study programme code: P0688D360001
Degree awarded: Ph.D.
Language of instruction: Czech
Accreditation: 14.5.2020 - 13.5.2030
Mode of study
Full-time study
Standard study length
4 years
Programme supervisor
doc. Ing. Radim Kolář, Ph.D.
Doctoral Board
Chairman :doc. Ing. Radim Kolář, Ph.D.Councillor internal :prof. Eric Daniel Glowacki, Ph.D.doc. Ing. Daniel Schwarz, Ph.D.doc. Mgr. Ing. Karel Sedlář, Ph.D.prof. Ing. Valentýna Provazník, Ph.D.Councillor external :prof. MUDr. Marie Nováková, Ph.D.prof. Pharm.Dr. Petr Babula, Ph.D.prof. Ing. Ladislav Janoušek, Ph.D.prof. Ing. Marek Penhaker, Ph.D.Ing. Pavel Jurák, CSc.
Fields of education
Study plan creation
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.
Issued topics of Doctoral Study Program
In the last decade, several semi-automated and robotic platforms for cultivation of microorganisms have emerged. Although they are equipped with integrated sensing and control systems to enable reproducible and scalable experiments, the cultivation parameters need to be set up manually.
This PhD project focuses on expanding the sensory capabilities of small-scale bioreactor platforms, such as Chi.Bio or Pioreactor, and integrating these measurements into an automated, adaptive control system. By synthesizing real-time data with precise hardware control, the project provides a framework to optimize bacterial cultivation conditions during data-driven fed-batch or continuous mode. Ultimately, the research aims to orchestrate these cultivations through a fully cybernetic bioinformatics approach, enabling the autonomous triggering of metabolic switches to drive the production of valuable secondary metabolites.
The project will be conducted at the Department of Biomedical Engineering, with anticipated collaboration with national (Faculty of Chemistry, BUT; Masaryk University) and international partners (HES-SO Valais Wallis, Switzerland; Polytechnic University of Valencia, Spain).
Your task:
Requirements:
We offer:
Supervisor: Sedlář Karel, doc. Mgr. Ing., Ph.D.
The topic of the doctoral thesis is focused on the development and evaluation of novel diffusion-perfusion magnetic resonance imaging methods for the characterization of microvascular flow and tissue microstructure. The main objective is to develop a methodology enabling the estimation of biomarkers describing the directionality and anisotropy of both perfusion and diffusion.
The work will include the design and optimization of acquisition schemes for multi-b-value and multi-directional measurements, suitable for both the IVIM method and advanced diffusion models such as DTI and possibly DKI. Furthermore, the work will involve the development and implementation of data processing methods enabling the estimation of diffusion and perfusion parameters, as well as derived quantitative indicators of flow directionality and tissue structure.
The proposed methods will be tested and validated using synthetic, phantom, preclinical, and clinical data. The work will be carried out at the Institute of Scientific Instruments of the Czech Academy of Sciences (9.4T MRI scanner, acquisitions on phantoms and laboratory animals) in collaboration with the University Hospital Brno (3T MRI scanner, acquisition of patient data).
Supervisor: Jiřík Radovan, doc. Ing., Ph.D.
The aim of this dissertation is to perform a comprehensive analysis of image data captured in pneumology. Specifically, it will involve data from an experimental video endoscope that captures lung parenchyma tissue. This is a technique currently under development that will bring new possibilities in the diagnosis and screening of lung diseases. This processing will involve the pre-processing, analysis, and classification of image data with the main focus on the detection of pathological tissue. In addition to this image data, the processing of traditional imaging techniques (classical lung X-rays, CT data, etc.) is also planned.
The work will be carried out as part of a project to develop and test an experimental video endoscope device, which is being carried out in collaboration with the Faculty of Electrical Engineering and Computer Science at the Technical University of Ostrava (FEI VŠB) and Van-Tec Medical company.
Supervisor: Mézl Martin, Ing., Ph.D.
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 mixed cultures are underdeveloped or completely missing.
This PhD project 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. It also offers opportunities 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 project will be conducted at the Department of Biomedical Engineering, with anticipated collaboration with national (Faculty of Chemistry, BUT; Masaryk University) and international partners (HES-SO Valais Wallis, Switzerland; LMU Munich, Germany).
Over the past decade, rapid advances in retinal imaging – particularly through adaptive optics (AO) – have brought a significant shift in the ability to achieve resolution down to the level of individual cells. This technological revolution enables in vivo study of previously unexplored microscopic structures of the retina, while at the same time creating the need for entirely new methods for processing such data. This PhD project focuses on a detailed analysis of retinal vasculature and the photoreceptor layer using modern algorithms, including advanced segmentation and detection of pathological changes, multimodal image registration from various sources, and automated image quality assessment and artefact suppression. The research will involve the use of deep learning for precise segmentation and classification of structures, the development of explainable AI for improved interpretation of results in clinical practice, the design of methodologies for longitudinal tracking of retinal microstructures to predict disease progression, and the application of multimodal imaging data in personalised medicine. The project offers opportunities to engage in ongoing research conducted in collaboration with the University of Leipzig, specifically in the areas of studying the effects of pregnancy on the ocular vascular system, evaluating the impact of new pharmacological therapies on retinal microstructure, and integrating data from multidimensional analyses (combining image-based features with clinical and functional parameters).
Your tasks:
Supervisor: Kolář Radim, doc. Ing., Ph.D.
The aetiology of neurodegenerative and cerebrovascular diseases remains incompletely understood, with chronic low-grade inflammation emerging as a key unifying mechanism. This persistent inflammatory state drives endothelial dysfunction, blood-brain barrier disruption, microglial activation, and progressive neuronal injury, yet the environmental triggers sustaining it remain largely unidentified. We hypothesize that lifelong exposure to nano- and microplastics (NMPs) and their accumulation in the human body constitute an unrecognized contributor to chronic inflammation. NMPs have been detected in human arterial plaques and brain tissue, where they are associated with elevated inflammatory cytokines and immune cell infiltration. At the cellular level, NMPs activate innate immune pathways, perpetuate inflammatory signaling, and induce oxidative stress—mechanisms consistent with those driving cerebrovascular and neurodegenerative disease pathology. This project aims to investigate whether cumulative NMP burden contributes to the onset and progression of neurological diseases using a mouse model chronically exposed to microplastics. We will optimize biological sample preparation for accurate NMP detection in human (blood, saliva, atherosclerotic plaques) and mouse tissues (blood, gut, liver, lungs, kidney, spleen, brain), apply spectroscopic and imaging methods, and develop (bio)sensors for detecting environmentally and biologically relevant plastics in saliva or blood. Integration of artificial intelligence and machine learning will support comprehensive data interpretation, ultimately guiding the development of point-of-care diagnostic devices for early detection of NMP-related disease risk.
Supervisor: Fohlerová Zdenka, doc. Mgr., Ph.D.
This PhD project focuses on the development and mechanistic understanding of high-frequency (>1 kHz) electrical stimulation waveforms for noninvasive neuromodulation of the central and peripheral nervous system. Building on our recent publications (DOI: 10.1038/s41467-025-64059-w; DOI: 10.1002/mds.30134), the project explores how kilohertz-range stimulation—both amplitude-modulated and unmodulated carriers—modulates neuronal activity in ways fundamentally different from conventional low-frequency sinusoidal or pulsed-current stimulation paradigms.
The PhD topic can span multiple levels of investigation, from biophysical mechanisms in neural tissue to translational studies in humans, and will depend on agreement. Experimental approaches include computational modeling, invertebrate animal models, studies in healthy human volunteers and select clinical populations. The project is highly interdisciplinary and involves close collaboration with clinicians and local hospitals.
Supervisor: Glowacki Eric Daniel, prof., Ph.D.
High-throughput sequencing technologies have fundamentally transformed the diagnosis of genetically driven diseases. While whole-exome sequencing (WES) enables comprehensive variant detection, its routine clinical use is still limited by challenges in data interpretation, particularly for variants of uncertain significance (VUS). At the same time, transcriptomic profiling by RNA sequencing (RNA-Seq) provides functional evidence that can substantially improve variant prioritization and pathogenicity assessment. However, robust computational frameworks for the systematic integration of genomic and transcriptomic data are still lacking.
This PhD project aims to develop advanced bioinformatics and machine-learning-based methods for integrative analysis of genomic and transcriptomic data to improve the interpretation of genetic variants and support personalized medicine. The research will focus on methodological development enabling a smooth transition from targeted gene panel sequencing to WES, while leveraging bulk RNA-Seq data to assess the functional consequences of genetic variants. Emphasis will be placed on algorithm design, in silico modeling, variant annotation, and multi-omics data integration in clinically relevant cohorts.
Main task:
The project will be carried out in close collaboration with CIIRC CTU, the First Faculty of Medicine of Charles University, and University Hospital Ostrava, providing access to real-world sequencing data and clinically relevant research questions.
Supervisor: Provazník Valentýna, prof. Ing., Ph.D.
Modelling of various signalling pathways became an integral part of holistic approach used in understanding of diverse microogranisms, especially after the emergence of frameworks allowing integration of multi-omics data.
This PhD project focuses on expanding our knowledge how particular omics technologies, e.g. genome sequencing, transcriptome sequencing, HPLC, mass spectrometry, etc., can be integrated into static and dynamic models describing behaviour of diverse organisms and their interactions with the environment. It particularly offers opportunities to explore interactions of antimicrobial agents, e.g., fungal pigments, with microbes causing food spoilage, e.g., bacteria from the genus Clostridium.
The project will be conducted at the Department of Biomedical Engineering, with anticipated collaboration with national (Department of Biotechnology, UCT Prague) and international partners from academia (LMU Munich, Germany; HZAU, China) and private sector (VTT, Finland).
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.
Supervisor: Harabiš Vratislav, Ing., Ph.D.
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.
Supervisor: Jakubíčková Markéta, Ing., Ph.D.
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.
Computed tomography (CT) is one of the most extensively used imaging modalities for the diagnosis of a wide range of diseases and pathological conditions. Recent advances in CT technology have led to the clinical deployment of modern scanners capable of multi-energy X-ray imaging through multilayer detector architectures and, in some cases, single-photon–level detection. These systems enable the generation of diverse parametric image types, including virtual monoenergetic images and material decomposition maps. Such capabilities substantially enhance the diagnostic value of CT imaging while enabling significant reductions in radiation dose, which is of major importance to the medical community.
This PhD research will focus on the development of advanced image processing and image analysis methods for multiparametric CT data acquired using multilayer detector systems, with a particular emphasis on machine learning and deep learning techniques. The student will design, implement, and rigorously validate algorithms for key tasks such as image preprocessing, segmentation, detection, classification, and outcome prediction, while explicitly addressing the specific properties and challenges associated with multiparametric CT images.
The goal of the project is to develop a comprehensive computer-aided diagnosis framework that improves diagnostic accuracy, robustness, and reproducibility, accelerates image interpretation, and reduces inter- and intra-observer variability as well as routine clinical workload.
The research will be carried out at the Department of Biomedical Engineering, in close collaboration with external partners. These include national clinical institutions (FN Brno, VFN Prague, FNUSA/ICRC Brno) and international industrial and research organizations (Philips Healthcare, The Netherlands; DKFZ Heidelberg, Germany). These collaborations will support clinical validation of the developed methods and enable continuous interaction with medical experts.
Supervisor: Chmelík Jiří, Ing., Ph.D.
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.
Supervisor: Vítková Helena, Ing., Ph.D.
Over the past decade, retinal imaging has advanced significantly, encompassing both anatomical and functional aspects such as flow, perfusion, blood velocity, and tissue oxygenation. These methods are crucial for diagnosing retinal and systemic diseases. This project focuses on developing an ophthalmic device and image processing techniques to evaluate retinal oxygenation and blood circulation. A basic setup of video-ophthalmoscope was designed and validated over three years. It can capture retinal video sequences at specific wavelengths and acquire various biosignals like electrocardiogram, photoplethysmographic, and respiratory signals. This project will contribute to interdisciplinary research covering retinal imaging, functional assessment, advanced image processing, and machine learning. The goal is to establish a methodology for evaluating retinal oxygenation and identifying potential biomarkers for disease diagnosis.
Project will be solved mainly at the Department of Biomedical Engineering. However, cooperation with our foreign partners is expected - University of Minnesota, USA and Lappeenranta University of Technology, Finland.
Background:
The receptor for advanced glycation end products (RAGE) plays a pivotal role in mediating neuroinflammation associated with Alzheimer's disease and neurodegeneration through both extracellular interactions (AGE binding) and intracellular signaling pathways. Recent findings indicate that the cytoplasmic tail of RAGE directly interacts with diaphanous-related formin-1 (DIAPH1), triggering a crucial signaling cascade that results in cytoskeletal rearrangement, NF-κB activation, and the production of reactive oxygen species (ROS). While traditional RAGE antagonists, such as Azeliragon, focus on inhibiting extracellular AGE interactions, their clinical success has been limited. In contrast, the intracellular RAGE-DIAPH1 axis presents an untapped therapeutic opportunity. This project harnesses structure-based computational drug design techniques, including molecular dynamics simulations, MM-PBSA/MM-GBSA binding free energy calculations, and QSAR modeling, to thoughtfully develop novel DIAPH1-RAGE antagonists. Promising lead compounds will undergo validation through cellular assays (measuring ROS and MAPK signaling), followed by in vivo evaluations in STZ-induced Alzheimer's disease models. This innovative approach zeroes in on a well-validated yet underexplored pathway, holding significant therapeutic promise for treating neurodegenerative diseases.
Responsibility: Ing. Jiří Dressler