Radosław Papis

received the engineering degree in Electronics in 2017 and the master's degree in Microsystems and Electronic Systems in 2023 from the Faculty of Electronics and Information Technology, Warsaw University of Technology.

Since 2016, he has been actively involved in international accelerator infrastructure projects, including the Phase Reference Line for the European Spallation Source (ESS) in Lund(Sweden), and the PIP-II accelerator(FermiLab, USA). He was responsible for the design and implementation of large-scale temperature stabilization systems for long rigid coaxial reference lines, including the development of measurement and actuation hardware, mechanical components, and integration with EPICS-based control systems.

His work has contributed to the long-term stable operation of reference signal distribution systems for accelerator synchronization. He also participated in the commissioning of accelerator subsystems such as Beam Loss Monitors and Beam Position Monitors.

His research interests include precision electronic systems, accelerator instrumentation, dielectric material characterization, microwave technologies, and interdisciplinary hardware design.

Natalia Wilczek

received her Bachelor of Engineering degree in Engineering and Data Analysis in 2024 from the Lublin University of Technology. She is currently pursuing a Master's degree in Data Analysis - Big Data at the Warsaw School of Economics (SGH). For six years, she has been actively developing her expertise in data analytics, successfully bridging academic research with professional practice. In her career within the digital technology and media sector, she is responsible for the econometric foundation and the design of advanced predictive models using R. She specializes in integrating classical statistical methodologies with modern Artificial Intelligence (AI) and Machine Learning solutions. In her analytical processes, she leverages Google Cloud Platform (GCP) and the Google ecosystem to build scalable solutions that support strategic business decision-making. Her contribution to ongoing projects focuses on transforming complex datasets into precise forecasts and operational optimization based on rigorous quantitative analysis. Her research interests include applied econometrics, predictive modeling, advanced cloud analytics, and the practical implementations of machine learning in dynamic business environments.