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Permanent magnets are vital parts of various devices, above all, those which are essential for the transition to a green society. However, they are made of materials, including rare-earth elements, for which unlimited availability cannot be taken for granted. Therefore, it is desirable to reduce the content of these materials …

As ecological, agricultural, and biological disciplines face mounting challenges like biodiversity loss, food chain disruption, and climate change, leveraging machine learning (ML) to process complex and heterogeneous data becomes increasingly vital. This dissertation explores the potential of ML in combination with explainability approaches for enhancing research in life sciences, specifically …

In the scope of this thesis, I focused on SrTiO3 (ST)-based dielectric ceramic composites as a lead-free perovskite material, which holds great potential for use in various electronic components. Such materials are large-scale and fabricated under high energy requirements, indicating an increasing need for a more environmentally friendly processing method. …

Complex systems, composed of numerous interacting subsystems, exhibit intricate dynamics that are challenging to model and understand. This dissertation focuses on advancing methodologies for detecting the direction and quantifying the strength of couplings in such systems and presents their application to real data. The basics of dynamical systems and chaos …

One of commonly used analytical technique for direct elemental analysis of solid samples is Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Although widely used for the analysis of various samples, obtaining accurate quantitative results remains a major challenge as matrix-matched standards are required. The aim of this thesis was …

Automatic terminology extraction, also known as automatic term extraction (ATE), is a natural language processing (NLP) task that identifies specialized terminology from domain-specific corpora. ATE is often used for terminographic tasks (e.g., the creation of specialized dictionaries) and contributes to several complex downstream tasks (e.g., machine translation and information retrieval). …

This thesis introduces a novel machine learning methodology for automatically assigning metadata to digitized artifacts in cultural heritage. Cultural heritage is an example of a domain that requires expert labeling, with few pre-existing labeled datasets and where simply getting more labeled data is challenging. The societal importance of cultural heritage …

The present thesis considers the premise that breath-holding in aquatic sports, primarily swimming, in order to enhance performance, can potentially cause adaptation to tissue hypoxia. To investigate this theory, a series of studies needed to be performed: I. Analysis of variability in individuals’ responses to hypoxia, to evaluate the range …

In this thesis, we introduce novel methods for equation discovery (ED), based on the use of probabilistic grammars. ED and symbolic regression address the task of finding a symbolic mathematical model that best describes observed data. Models can be as simple as an algebraic equation or as complex as a …

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a versatile analytical method widely used in the field of surface science and thin films. Although it gives elemental, molecular, and isotopic information, it has a very low detection limit and high lateral resolution, is fast and works with all the vacuum-compatible samples, …