Stefan Borkovski

Engaged in core Data Science team, leading and contributing to various projects related to energy trading: Designing and implementing ETL pipelines. Time series forecasting and modelling of energy systems: − Electricity load modelling and water level forecasting. − Multivariate analysis − Trade classification algorithms Developing ROI simulations and trading strategies. Performing various energy market analyses. Visualizations.

Part of the research department focuses on integrating system identification and machine learning to pioneer advanced techniques for monitoring the operational efficacy of solid oxide electrolysers and fuel cell systems. Developed intricate algorithms to assess the internal integrity of these devices, in collaboration with the Institute for Thermal Engineering at TU Graz, Austria. The findings of the research were presented through a conference paper.

Developed an algorithm for 3D sample modelling to create detailed models of minor structures. Contributed to the project's progression to its final testing phase, which I found immensely rewarding. Collaborated with a skilled team of professionals in the NanoCT Systems field, substantially bolstering my teamwork and professional capabilities.

Prediction of electricity consumption using various dependent variables such as weather, holidays, economic factors, etc. Results were approved for publication after being correlated with an electricity trade market, showcasing the benefits of such a system. Gained valuable experience in Data preprocessing, Time series analysis, and Neural network forecasting.

Gained hands-on experience in the electronic engineering and analysis departments, tackling electrical and mechanical challenges in the automotive industry. Was part of the development of innovative testing software and hardware solutions. Acquired valuable insights into the automation of SMT production lines and image-based product test engineering practices.

As a part of this school, I am learning how one science community works and how to contribute to it. While following an exponential learning curve and working toward a master's degree I am passionately assisting research for real-life projects.

GPA: 9.29/10, (244 ECTS). Thesis: "Solving the Inverse Kinematics Problem of Robotic Arm using Machine Learning Algorithms". Principal subjects covered: Programming, Machine Learning, Mathematics, Optimal controllers and state estimators, Automation of production plants and processes, Robotics.