Advanced Methods for Uncertainty Analysis in Technical and Biomedical Measurements

PhD study program: Measurement Technology
Academic year: 2026-2027
Advisor: doc. RNDr. Viktor Witkovský, CSc. (viktor.witkovsky@savba.sk)
External educational institution: Institute of Measurement Science SAS
Accepting university: Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Institute of Electrical Engineering

Annotation:

The dissertation focuses on the development, analysis, and experimental validation of advanced probabilistic, statistical, and numerical methods for quantifying measurement uncertainty, with emphasis on both technical and biomedical applications. The objective is to establish a consistent methodological framework for the identification, modeling, and propagation of uncertainties in complex measurement tasks, including multi-source uncertainty, correlated quantities, and models with errors-in-variables. The methodological development will include Monte Carlo simulation approaches, regression models with errors-in-variables (EIV) and their generalizations, as well as probabilistic methods based on characteristic functions for the accurate determination of measurement error distributions. Special attention will be devoted to biomedical measurements, where uncertainties substantially influence the interpretation of experimental results, diagnostic decision-making, and the assessment of intervention effectiveness. The proposed methods will be applied to real-world data within the ERA-NET NEURON project (NeuroPain), aimed at identifying individual brain signatures of chronic pain and optimizing non-invasive neuromodulation using focused ultrasound (FUS). Statistical models will be employed to quantify the effects of intervention strategies, analyze the relationship between FUS targeting accuracy and changes in neuroimaging and behavioral indicators, and evaluate the robustness of conclusions under measurement uncertainty. The outcome of the thesis will be a generally applicable methodological framework for uncertainty analysis in complex measurement systems, with applications in electrical engineering, metrology, and biomedicine. The work will include the implementation of the proposed algorithms (e.g., in MATLAB, Python, or R) and their experimental validation using real data. The research will be conducted at the Institute of Measurement Science of the Slovak Academy of Sciences, Bratislava, in cooperation with international partners.