The postgraduate study programme aims at preparing top scientific and research specialists in various areas of mathematics with applications in electrical engineering fields of study, especially in the area of stochastic processes, design of optimization and statistic methods for description of the systems studied, analysis of systems and multisystems using discrete and functional equations, digital topology application, AI mathematical background, transformation and representation of multistructures modelling automated processes, fuzzy preference structures application, multicriterial optimization, research into automata and multiautomata seen in the framework of discrete systems, stability and system controllability. The study programme will also focus on developing theoretical background of the above mentioned areas of mathematics.

The graduates of the postgraduate study programme Mathematics in Electrical Engineering will be prepared for future employment in the area of applied research and in technology research teams. Due to the comprehensive use of computer engineering throughout the study programme, the graduates will be well prepared for work in the area of scientific and technology software development and maintenance. The graduates will also be prepared for management and analytical positions in companies requiring good knowledge of mathematical modelling, statistics and optimization.

The graduates of the postgraduate study programme Mathematics in Electrical Engineering will be prepared for future employment in the area of applied research and in technology research teams. Due to the comprehensive use of computer engineering throughout the study programme, the graduates will be well prepared for work in the area of scientific and technology software development and maintenance. The graduates will also be prepared for management and analytical positions in companies requiring good knowledge of mathematical modelling, statistics and optimization.

doc. RNDr. Zdeněk Šmarda, CSc.

1. Modelling of processes by systems input-output and neural networks

   In the project will be studied input-output systems with structured input and output objects. In the connection with multiautomata considered as actions of binary hyperstructures on suitable state objects will be constructed structures apllied onto investigation of transformations of signals and processes in the connection with construction of trransfer neural networks.

   Tutor: Chvalina Jan, prof. RNDr., DrSc.

2. Modelling of processes by systems input-output and neural networks

   In the project will be studied input-output systems with structured input and output objects. In the connection with multiautomata considered as actions of binary hyperstructures on suitable state objects will be constructed structures apllied onto investigation of transformations of signals and processes in the connection with construction of trransfer neural networks.

   Tutor: Chvalina Jan, prof. RNDr., DrSc.

3. Semianalytical solution methods for fractional functional differential equations

   The aim of the doctoral thesis is to propose modifications of advanced semianalytical solution methods as, for example, the differential transformation method or homotopy perturbation methods for solving of initial value problems and boundary problems of fractional functional differential equations including partial differential equations with applications in control theory.

   Tutor: Šmarda Zdeněk, doc. RNDr., CSc.

4. Topological methods and properties in mathematical information and causal structures

   The dissertation will be focused on the study and development of certain suitable topological methods for the work with the mathematical structures, carrying some information.The research will be concentrated especially on the properties and the relationships of causal character. Possible applications are, among others, e.g. in computer science (concurrent and parallel processes), cybernetics, quantum information theory and physics (some aspects of general relativity versus quantum gravity).

   Tutor: Kovár Martin, doc. RNDr., Ph.D.