Full-time study

4 years

prof. Ing. Martin Hartl, Ph.D.

Chairman :
prof. Ing. Martin Hartl, Ph.D.
Councillor internal :
prof. Ing. Petr Stehlík, CSc., dr. h. c.
prof. Ing. Josef Štětina, Ph.D.
prof. Ing. Jiří Pospíšil, Ph.D.
doc. Ing. Petr Blecha, Ph.D., FEng.
prof. Ing. Pavel Hutař, Ph.D.
prof. Ing. Radomil Matoušek, Ph.D.
doc. Ing. Jaroslav Katolický, Ph.D.
doc. Ing. Jaroslav Juračka, Ph.D.
Councillor external :
Ing. Jan Čermák, Ph.D., MBA

The main goal of the doctoral study programme is, in accordance with the Higher Education Act, to train highly qualified and educated professionals who are capable of independent scientific, research and creative activities in the field of design and process engineering. The graduates are equipped with knowledge and skills that enable them to work at Czech or international academic institutions or research institutes. The programme focuses on theoretical knowledge as well as practical experience in the field of doctoral studies. Cooperation with international research institutes is highly supported. The study programme is designed to fulfil demands and meet societal and industry requirements for highly educated and qualified professionals in the fields of design and process engineering.
Doctoral study programme is primarily based on research and creative activities of doctoral students. These activities are intensively supported by student participation in national and international research projects. Research areas include design (analysis, conception, design of machinery, vehicles, machine production and energy) and process engineering (analysis, design and projection of processes in the engineering, transport, energy and petrochemical industries).

A graduate of the doctoral study programme is a highly qualified expert with broad theoretical knowledge and practical skills, which enables him/her to carry out creative and research activities both independently and/or in a scientific team. The graduate is acquainted with current findings in the field of design and process engineering and is able to apply the knowledge in his/her research or creative activities. The graduate is also able to prepare a research project proposal and to oversee a project. At the same time, the graduate is able to make use of theoretical knowledge and transfer it in practice. Moreover, the graduate can adapt findings from related disciplines, cooperate on interdisciplinary tasks and increase their professional qualifications. The graduate participation on national and international researches and cooperation with international research institutions contributes to higher level of their professional competences. This experience allows graduates not only to carry out their own scientific activities, but also to professionally present their results, and to take part in international discussions.
The graduate can demonstrate knowledge and skills in three main areas and the synergy produces great outcomes.
1. Broad theoretical knowledge and practical skills closely related to the topic of the dissertation (see below).
2. Professional knowledge and skills necessary to carry out scientific work, research, and creative activities.
3. Interpersonal and soft skills and competencies - the graduate is able to present their ideas and opinions professionally, is able to present and defend the results of their work and to discuss them and work effectively in a scientific team or to lead a team.
According to the topic of the dissertation, the graduate will acquire highly professional knowledge and skills in mechanical engineering, in particular in design and operation of machines, machinery, engineering processes and vehicles and transport vehicles. Thanks to the broad knowledge and skills, graduates can pursue a career in research institutes in the Czech Republic and abroad, as well as in commercial companies and applied research.

A graduate of the doctoral study programme is a highly qualified expert with broad theoretical knowledge and practical skills, which enables him/her to carry out creative and research activities both independently and/or in a scientific team. The graduate is acquainted with state-of-the-art findings in the field of design and process engineering and is able to apply the knowledge in his/her research or creative activities. The graduate is also able to prepare a research project proposal and to oversee a project. At the same time, the graduate can make use of theoretical knowledge and transfer it in practice. Moreover, the graduate can adapt findings from related disciplines, cooperate on interdisciplinary tasks and increase their professional qualifications. The graduate typically finds a job as a researcher, academic personnel, computer scientist or designer. The graduate is also well equipped with skills and competences to perform well in managerial positions.

See applicable regulations, DEAN’S GUIDELINE Rules for the organization of studies at FME (supplement to BUT Study and Examination Rules)

The rules and conditions of study programmes are determined by:
BUT STUDY AND EXAMINATION RULES
BUT STUDY PROGRAMME STANDARDS,
STUDY AND EXAMINATION RULES of Brno University of Technology (USING "ECTS"),
DEAN’S GUIDELINE Rules for the organization of studies at FME (supplement to BUT Study and Examination Rules)
DEAN´S GUIDELINE Rules of Procedure of Doctoral Board of FME Study Programmes
Students in doctoral programmes do not follow the credit system. The grades “Passed” and “Failed” are used to grade examinations, doctoral state examination is graded “Passed” or “Failed”.

Brno University of Technology acknowledges the need for equal access to higher education. There is no direct or indirect discrimination during the admission procedure or the study period. Students with specific educational needs (learning disabilities, physical and sensory handicap, chronic somatic diseases, autism spectrum disorders, impaired communication abilities, mental illness) can find help and counselling at Lifelong Learning Institute of Brno University of Technology. This issue is dealt with in detail in Rector's Guideline No. 11/2017 "Applicants and Students with Specific Needs at BUT". Furthermore, in Rector's Guideline No 71/2017 "Accommodation and Social Scholarship“ students can find information on a system of social scholarships.

1. round (applications submitted from 01.04.2026 to 31.05.2026)

1. Artificial intelligence in electric vehicles

   The dissertation focuses on the application of artificial intelligence methods in the development of electric vehicle powertrain control, addressing both vehicle dynamics and energy management, including thermal management. The research will concentrate on the design and implementation of advanced algorithms based on machine learning and adaptive control, aimed at optimizing torque distribution, traction control, energy recuperation, and overall intelligent energy flow management, including thermal management strategies. The proposed methods will be implemented on an experimental or production-oriented platform and validated using simulation models and real-world operational testing.

   Supervisor: Kučera Pavel, doc. Ing., Ph.D.

2. Calibration and validation of CFD models for solid-particle erosion in curved flow geometries

   The aim of the doctoral thesis is to develop and validate a numerical (CFD) methodology for predicting erosion caused by solid particles in flows over curved walls and in technically relevant geometries. The student will work with multiphase modelling (Euler–Lagrange/DPM), including the selection of appropriate boundary conditions, turbulence modelling, and particle–wall interaction. An important part of the work will be the calibration of erosion-model parameters (e.g., Finnie/Tabakoff) against experimental data and a sensitivity analysis of these parameters. The thesis will also include uncertainty quantification and the definition of applicability limits for different materials, particle sizes and concentrations, and operating conditions. The main outputs will be validated computational procedures and recommendations for transferring results from laboratory configurations to more complex real-world geometries (e.g., blade passages of turbines and pumps).

   The thesis will be carried out within an international GAČR project in collaboration with the University of Ljubljana (Slovenia), enabling research stays, conference travel, and other international activities.

   Supervisor: Rudolf Pavel, doc. Ing., Ph.D.

3. Cavitation erosion of fuel-pump components: accelerated testing, damage quantification, and a validated wear model

   The aim of the thesis is to experimentally quantify cavitation damage on selected pump components and to develop a methodology for reliably translating accelerated test results into erosion-risk estimates under real operating conditions. The student will focus on designing and evaluating experiments (including instrumentation), detailed measurement and description of damaged surfaces, and the creation of comparable erosion maps. Based on the acquired data, a cavitation-erosion model will be developed and validated for assessing design variants and operating regimes. A practical extension may also be to link the results with diagnostics by identifying relationships between the intensity of cavitation/erosion and measurable test signals (e.g., vibroacoustics) to enable early cavitation detection during testing.

   Supervisor: Rudolf Pavel, doc. Ing., Ph.D.

4. Closed-loop robot control system using machine learning based on 3D data

   Robotic manipulation in real-world environments requires continuous adaptation to variability in object geometry, positioning, and the progression of technological processes. Traditional robotic systems are based on pre-programmed trajectories, precise calibration, and manually designed sensor data processing methods, which limits their robustness and ability to generalize across tasks such as grasping, assembly, or material joining processes.

   This work focuses on a closed-loop robot control system using machine learning based on 3D data, in which data from RGB-D sensors are used to learn task-relevant scene representations that directly influence robot control and motion adaptation. This approach integrates perception and control into a unified framework, where 3D sensory data serve as a source of visual feedback enabling online correction of trajectories and robotic skills.

   The system enables estimation of deviations, uncertainties, and affordances based on spatial data and their use for adaptive real-time robot control. The work will address not only manipulation tasks, but also precise technological processes such as soldering and welding, as well as object grasping tasks.

   Supervised learning, self-supervised learning, and reinforcement learning methods will be explored using both real and simulated data, enabling scalable training without the need for extensive manual annotation. By integrating 3D-data-driven machine learning with closed-loop robot control, this research aims to increase the robustness, flexibility, and autonomy of robotic manipulation in real industrial applications.

   Supervisor: Škrabánek Pavel, doc. Ing., Ph.D.

5. Design of Decomposition Strategies for Efficient Solving of Complex Transportation Problems

   The dissertation will focus on the decomposition of complex problems solved using heuristic algorithms, with particular attention to transportation problems addressed at the Institute of Process Engineering. The study will include an analysis of the key characteristics of these complex problems and their impact on computational complexity. Emphasis will be placed on understanding the problem structure and identifying parts suitable for decomposition into smaller, more manageable subproblems. Based on this analysis, a decomposition methodology will be designed and developed to enable more efficient problem-solving. This approach will be tailored to the specifics of transportation problems, including their dynamic nature, interdependencies between problem components, and the need for rapid decision-making. The dissertation will also include the design and implementation of algorithms utilizing parallel processing to maximize the efficient use of available computational resources. The implementation will be adapted to the specific requirements of individual problems, aiming to optimize both the accuracy of results and computational time. The findings of this dissertation will contribute to the development of new methods for solving 

   Supervisor: Šomplák Radovan, doc. Ing., Ph.D.

6. Digital twin of an electric vehicle powertrain including thermal management

   The dissertation focuses on advanced modelling of the powertrain of modern electric vehicles with emphasis on the combination of 0D, 1D and 3D models and analysis of their thermal behaviour. This approach enables a comprehensive understanding of the dynamics and efficiency of the powertrain of electric vehicles, which is crucial for optimizing their performance and durability. A strategy is proposed to optimize the thermal management and drivetrain efficiency based on the results. Translated with www.DeepL.com/Translator (free version)

   Supervisor: Štětina Josef, prof. Ing., Ph.D.

7. Digital Twin of Military Convoys and Road Infrastructure for Planning, Simulation, and Passability Assessment under Crisis Mobility Conditions

   The dissertation would focus on the design and validation of a multi-layer digital twin that integrates:

   • a digital model of a military convoy (vehicle types, masses, dimensions, dynamics, spacing, and driver/control system behavior),

   • a digital twin of road infrastructure (route geometry, bridges, load capacities, restrictions, intersections, gradients, and pavement surface),

   • an operational and scenario layer (peacetime transit, crisis mobility, degraded conditions, passage restrictions, and detours),

   • an evaluation layer (passability, safety, time delays, infrastructure loading, and risks).

   The objective would be to develop a tool that enables virtual testing of military convoy movement on a real road network and assessment of where infrastructure limits occur, as well as how to optimize routing, convoy configuration, and logistics planning.

   Supervisor: Štětina Josef, prof. Ing., Ph.D.

8. Effect of surface structure on boundary layer properties, cavitation inception and cavitation erosion

   The aim of the dissertation is to investigate the influence of differently shaped and differently spaced structures on the surface of a hydraulic profile on its hydraulic and cavitation characteristics. The work will be carried out first using computational simulations based on hybrid approaches to turbulence modelling, followed by experimental validation in the fluid engineering department's cavitation tunnel. Applications can be found in hydraulic machine blades or on the functional surfaces of various hydraulic devices (e.g. valves).

   Supervisor: Rudolf Pavel, doc. Ing., Ph.D.

9. Electric motors for the aerospace industry

   The goal of the topic is the design development of an electric motor using a structured magnetic circuit produced by the method of 3D metal printing. It is expected that a suitable design of the structured magnetic circuit should increase the efficiency of the electric motor, reduce its weight and at the same time improve cooling. The design of the magnetic circuit will be based on the patented technology of the Department of Technical Diagnostics (EP3373311).

   Supervisor: Kubík Michal, doc. Ing., Ph.D.

10. Hybrid physical-data modeling of high-speed machines with application to structural optimization

    The dissertation focuses on creating a general framework for modeling rotating machines (turbochargers, electric motors, etc.) that systematically combines classical mechanics with modern data-oriented methods and artificial intelligence tools.

    The aim is to extend the traditional approach based on direct numerical solution of equations of motion with a methodology that will enable physically consistent integration of data-identified members into the structure of the dynamic model. The work will be based on the formulation of equations of motion and the use of approximation tools based on machine learning.

    At the same time, a methodology for model order reduction and parameter identification will be developed using physically informed neural networks and probabilistic approaches so that the resulting models can be used in the design and optimization process. The proposed framework will be verified on a selected technical system in direct cooperation with industrial practice.

    During the course of study, close cooperation with an industrial partner and the practical application of the results of the work are expected. The study includes a long-term internship at a world-renowned research center abroad, regular participation in international conferences in the field, and publications in professional journals.

    Supervisor: Novotný Pavel, prof. Ing., Ph.D.

11. Integration of Machine Learning Methods into Adaptive Control of Industrial Robotic Workcells

    The dissertation will focus on the development of an advanced control architecture for industrial robotic workcells by integrating modern artificial intelligence models, in particular multimodal vision-language and vision-language-action models. The objective is to enable adaptive interpretation of visual data, sensory inputs, and natural language instructions, and their transformation into a formal representation of manufacturing tasks executable by an industrial robot. The research will aim to bridge data-driven foundation models with classical motion planning, control methods, and formal verification techniques to achieve a higher level of autonomy while preserving safety, determinism, and compliance with the technological constraints of the workcell.

    Supervisor: Hadaš Zdeněk, prof. Ing., Ph.D.

12. Integration of Reinforcement Learning into Gearshift Strategy Design

    The aim of the thesis is to develop a methodology for integrating feedback-based learning into the development of gear-shifting algorithms for transmission systems (AT/DCT/AMT, or an e-axle with a multi-speed gearbox), so that shifting can adaptively respond to driving style, vehicle load, route profile, and component degradation. The work will combine model-based design approaches (MPC/DP, rule-based logic) with feedback learning methods (reinforcement learning, offline RL, safe learning) and will validate the results in a digital-twin environment (powertrain and longitudinal vehicle dynamics simulation) and on experimental data.

    Supervisor: Štětina Josef, prof. Ing., Ph.D.

13. Intelligent algorithms in autonomous vehicles

    The dissertation focuses on the research and enhancement of advanced perception algorithms for autonomous vehicles, with particular emphasis on operation under adverse weather conditions (rain, snow, fog, and low-visibility scenarios). The research will concentrate on the design of artificial intelligence-based methods, particularly deep learning and multi-sensor data fusion (camera, radar, LiDAR), with the aim of increasing the robustness and reliability of environmental perception. The primary objective is to improve object detection and classification, localization, and prediction of road users’ behavior in situations where the quality of sensor data is significantly degraded. The system will be implemented using dedicated automotive-grade platforms and validated both in real-world operation and in advanced simulation environments.

    Supervisor: Kučera Pavel, doc. Ing., Ph.D.

14. Liquid and steam flow dynamics and distribution in a desorber in the system of CCUS (Carbon Capture, Utilization and Storage.

    To study experimentally and computationally the phenomena that arise during the distribution of a liquid (solvent) in a rotating bed of a desorber, where the absorbed CO2 is released, which is subsequently compressed and transported for further use. The desorber is part of the entire system to produce biomethane from biogas in biogas stations. The release of CO2 from a liquid solvent is energy-intensive and requires very efficient equipment. An advanced solution is the use of various rotating geometric structures (RPB - Rotating Packed Bed).

    The subject of the doctoral study will be to assess the influence of various geometric structures of the TPMS type - Triply Periodic Minimal Surfaces (such as gyroids, diamonds and the like) on the heating of their surfaces using steam and the dispersion of liquid solvents on their surfaces with the aim of finding the best solution in terms of energy consumption. In terms of practical steps and solution methods, the doctoral student will prepare an experimental stand in cooperation with the team in the rotating technologies laboratory and will study the phenomena described above using experiments and computational simulations. Energy consumption, the amount of released CO2 and the possibility of heat recovery after steam condensation will be monitored.

    The study will be part of the solution of the trilateral cooperation project with the Łódź University of Technology (Poland) and TU Berlin (Germany) and the Theta II Technology Agency project. The doctoral student will participate in the meetings of both projects and others that are in the preparation phase. It will be possible to pay the student a scholarship or pay him a salary in another form from both sources. In the solution, the doctoral student will closely cooperate with colleagues from Łódź and Berlin, where there is also the possibility of completing an Erasmus internship. It is also possible to complete it, for example, at Newcastle University or other cooperating universities.

    Supervisor: Jícha Miroslav, prof. Ing., CSc.

15. Multidisciplinary Design and Optimisation of Small Propellers for Unmanned Aerial Vehicles

    The doctoral research topic focuses on small propellers for unmanned aerial vehicles. The rapid growth of the UAV market brings increasing demands for sustainability, which is reflected, among other aspects, in the requirement for higher energy efficiency of propulsion systems, particularly propellers. At the same time, the progressive integration of unmanned aerial vehicles into regular airspace operations, including missions in proximity to populated areas, imposes stricter requirements on noise reduction.

    The dissertation will apply a Multidisciplinary Design and Optimisation methodology with the aim of advancing the current state of the art in small propeller design through an integrated aerodynamic, acoustic, and structural approach. The expected outcome is a design framework enabling systematic optimisation of propellers with respect to efficiency, noise emissions, and operational constraints.

    Supervisor: Zikmund Pavel, doc. Ing., Ph.D.

16. Non-exhaust emissions from motor vehicl

    Your dissertation will address a timely and important topic that has a direct impact on the environment and public health. Emissions from tyres and braking systems represent a significant source of pollution that is often neglected alongside traditional emissions from vehicle exhaust systems. These particles can vary in size and chemical composition, making them difficult to monitor and control. The main challenge will be to develop predictive models for the release of these particles. Translated with www.DeepL.com/Translator (free version)

    Supervisor: Štětina Josef, prof. Ing., Ph.D.

17. Numerical modelling of flow and cavitation in centrifugal fuel pumps and hydraulic design modifications

    The thesis will focus on developing and validating CFD methodologies for cavitation prediction in a real fuel-pump geometry, including the selection of an appropriate multiphase approach, turbulence modelling, and boundary conditions. The student will compare simulations with experimental data from performance and endurance tests and progressively refine the model so that it can be used to design hydraulic modifications. The main outputs will be recommendations on which design interventions most effectively mitigate cavitation while maintaining the required performance and efficiency. The doctoral research will be carried out within an applied research project in collaboration with an industrial partner.

    Supervisor: Rudolf Pavel, doc. Ing., Ph.D.

18. Optimization of a CO₂ Air-Conditioning and Heat Pump System Using a System-Level Model

    Build and use a CO₂ system model as a platform for “virtual prototyping” and optimization. Design component-level optimizations specifically for CO₂ systems, and quantify the benefits of these improvements at the overall system level. Generalize the findings and develop a full digital twin of the CO₂ system. Internal Heat Exchanger (IHX): A detailed analysis of the IHX impact on system performance across different operating modes. Optimization of its size and design using the model.
    Compressor and Expansion Valve: Modeling the interaction of these components and their influence on performance and COP. Assessing the effects of, for example, a different compressor type or a different expansion-valve characteristic.

    Supervisor: Štětina Josef, prof. Ing., Ph.D.

19. Optimization of Hybrid Powertrain

    The aim of the doctoral dissertation is to develop and experimentally validate a methodology for optimizing the behavior of full-hybrid powertrains, with a focus on energy flow management, selection of operating modes, and calibration of control strategies to achieve minimal fuel consumption and emissions while maintaining driving comfort, performance, and long-term battery durability. The work will be based on the development and refinement of multi-level simulation models of the powertrain and vehicle (e.g., 1D/0D models of the engine, electric motor, transmission, and battery and their integration into a longitudinal vehicle dynamics model), analysis of real-world driving data, and implementation of advanced optimization approaches (e.g., multi-objective optimization, predictive control, and potentially machine learning) to design robust strategies under various driving scenarios and temperature conditions. The solution will include validation of the models and strategies through experimental testing and the formulation of recommendations for practical application, including quantification of benefits and sensitivity analyses; the outputs will comprise published results, a validated methodology, parameterized models, and a set of test scenarios for repeatable evaluation of hybrid control strategies.

    Supervisor: Štětina Josef, prof. Ing., Ph.D.

20. Research and development of high-speed rolling bearings for extreme operating conditions

    The objective of the dissertation is to develop an experimental platform and conduct original research on high-speed rolling bearings operating at extreme rotational speeds and under non-standard lubrication regimes. The work will focus on the physical mechanisms of lubrication at very high rotational frequencies, including fuel-lubricated operation and minimum lubrication conditions, and their influence on bearing temperature, dynamic stability, and service life. The expected outcomes include new design and tribological principles applicable to the propulsion units of unmanned aerial vehicles and other extreme-performance applications.

    Supervisor: Omasta Milan, doc. Ing., Ph.D.

21. Research of design and process properties of triply periodic minimal surface structures

    TPMS, i.e. Triply Periodic Minimal Surface, is a general term in technical practice for specific groups of additively manufactured approximations of surface-symmetric structures. These (currently only additively manufacturable) specific surface structures use symmetries inspired by crystallographic groups (for example, cubic, tetragonal, rhombohedral and others) and are currently widely used not only in art, but also in a number of progressive technical disciplines (such as in the field of modeling bone substitutes, architecture, industrial design or in the construction of mechanical equipment). In the field of process technologies, the use of TPMS structures is offered in a wide range of modern equipment for heat or mass transfer. However, the problem is insufficient knowledge of the fundamental process properties (typically thermal-hydraulic properties) of modern TPMS structures in connection with the limited possibilities of their manufacturability (3D metal or plastic printing) and structural design (i.e., for example, roughness, thickness and curvature of surfaces) as well as their modeling or computational prediction of their process behavior. A completely unexplored area from this perspective is the use of gradient TMPS structures, in which the relative density of the structure changes within one part. The aim of the dissertation is therefore experimental research and computational modeling of the essential design and process properties of selected perspective TPMS structures, which can be produced by 3D metal or plastic printing. The topic is part of the currently ongoing OP JAK project.

    Supervisor: Jegla Zdeněk, prof. Ing., Ph.D.

22. Robust and efficient detection of small objects in images with emphasis on practical deployment

    The aim of this dissertation is to propose and develop new approaches for detecting objects in images that occupy only a small portion of the overall image area. The work will focus on methods that enable reliable identification of such objects even under challenging conditions, where traditional techniques may be insufficient. The research will include an analysis of existing methods, identification of their limitations, and the design of new solutions aimed at improving the accuracy, robustness, and efficiency of detection. Special attention will be given to evaluating computational complexity and inference time with respect to practical deployment. The dissertation will also involve experimental validation of the proposed approaches and an assessment of their applicability in real-world scenarios.

    Supervisor: Škrabánek Pavel, doc. Ing., Ph.D.

23. Smart suspension system for military vehicles

    The aim of the doctoral dissertation is to develop an electronically controlled suspension system using magnetorheological technology for military vehicles. The work will include the design of a control system and experimental validation on a vehicle under real operating conditions. The research focuses on improving off-road mobility, enhancing driving stability, and stabilizing the vehicle during firing.

    Supervisor: Kubík Michal, doc. Ing., Ph.D.

24. Software-Defined Vehicle

    The dissertation focuses on advanced modeling of Software-Defined Vehicles (SDVs) with an emphasis on integrating multiphysics simulation approaches in 0D, 1D, and 3D modeling environments. The goal is to develop a digital twin of the vehicle, enabling real-time analysis of dynamics, energy efficiency, and operational characteristics. By leveraging advanced algorithms, machine learning, and cloud computing simulations, key aspects of vehicle performance, safety, and longevity can be optimized. This approach reflects current trends in the automotive industry, where software-defined architecture plays a crucial role in transforming vehicles towards greater autonomy, connectivity, and efficiency.

    Supervisor: Štětina Josef, prof. Ing., Ph.D.

25. Sustainable drone design

    The doctoral research topic focuses on the design of an unmanned aerial vehicle with regard to the current European Union requirements for sustainability and carbon neutrality. The dissertation will employ the Life Cycle Assessment methodology to evaluate the environmental impact of both the structural design and the operation of such systems. The research aims to contribute to the future standardisation of unmanned aerial vehicles by proposing a set of recommendations for their standardisation and quantifying their environmental impact.

    Supervisor: Zikmund Pavel, doc. Ing., Ph.D.

26. Sustainable Machine Vision

    The dissertation will focus on exploring ways to enhance sustainability in the field of machine vision. Sustainability in machine vision encompasses the optimization of data acquisition, transmission, and processing to reduce energy and material consumption. The specific focus of the dissertation will be determined based on a state-of-the-art analysis conducted by the student. Preference will be given to the direction with the highest research and application potential.

    Supervisor: Škrabánek Pavel, doc. Ing., Ph.D.

27. 4D printing of magnetically active elastomers

    Intensive research and development is currently underway in the field of magnetically active elastomers or hydrogels, which can be produced using so-called 4D printing. 4D printing is a new and completely unique technology that allows printing dynamic 3D structures capable of changing their shape over time. This topic aims to develop equipment and methodology for 4D printing of magnetically active elastomers and hydrogels. Part of the work will be the application of this technology to the issue of micro-robotics.

    Supervisor: Kubík Michal, doc. Ing., Ph.D.