Mgr. Vojtěch Rybář

2023 - 2025

The project focuses on a broad area of basic research on algorithms, concepts and techniques in knowledge engineering. This is a promising and constantly expanding field both in terms of theoretical research and in terms of potential for practical applications. At the same time, the area of the project is experiencing an extreme increase in complexity due to the amount of data and the diversity of techniques that are trying to deal with this trend. The central interest of the project will specifically be methods in data processing, extraction of information-rich data from data and automatic search for explanations for phenomena that occur in the data. A burning problem of current artificial intelligence focused on knowledge processing is the limited ability or inability to provide an explanation leading to a certain decision. As a result, decisions often cannot be traced back to their origin, which makes debugging difficult and reduces the credibility of the system. This question will therefore be reflected throughout the entire project through all sub-themes. It is a doctoral project covering the diverse research interests of the doctoral student members, but providing a general supporting question that intertwines across sub-themes. The project specifically intends to develop algorithms and techniques in neural networks, evolutionary algorithms, image and text processing, and motion information processing, from basic theory to practical applications. The project immediately follows the ending SGS project on modern algorithmic techniques for knowledge engineering.

2023

Předmětem Inovačního voucheru je automatický systém pro detekci úniků z potrubí pomocí aplikace strojového učení a umělé inteligence.

2024 - 2026

The FOCAL project aims to advance our understanding of complex Earth systems and mitigate climate change impacts through five main objectives: developing a high-performance computing platform for better climate and earth system modelling; creating AI modules for efficient climate data exploration; designing web-apps to support decision-making; conducting pilot studies to address assessment challenges and demonstrate app benefits; and fostering a collaborative network across research, data providers, data science, and HPC infrastructure. By combining expertise and integrating data from multiple sources, FOCAL aims to improve climate models, inform policy decisions, and support sustainable development.

2024 - 2026

PoliRuralPlus

2024 - 2026

Using AI to integrate satellite and climate data with weather forecasts and sensor measurements to better adapt agriculture to climate change, reducing economic costs and negative environmental impacts.