About Me

As a committed engineer, my expertise spans AI research, Data Science, and embedded systems engineering. My professional trajectory reflects a dedication to acquiring knowledge, advancing my skills, and expanding my professional horizons. I am deeply passionate about leveraging the transformative potential of data and algorithms to address complex challenges and drive technological innovation in diverse industries. Anchored by a philosophy of perpetual learning, I endeavor to remain at the cutting edge of AI and Data Science, contributing to the field's evolution.

Practical Experience

Freelancer, Data Science and Machine Learning
Jul. 2023 - Now
Application of recent research in the area of artificial symbolic music generation.
Junior Researcher, FH Salzburg GmbH
Oct. 2021 - Now
Salzburg, AT
Research on applications of persistent homology in contrastive learning.
Lecturer (C. Programming) and Tutor (Math. 1 and 2)
Development Engineer, Novanta Europe GmbH
Mar. 2021 - Aug. 2021
Wackersdorf, GER
Embedded software development for ARM-based microcontrollers, CI with Jenkins.
Dual Course of Study, Novanta Europe GmbH
Mar. 2017 - Feb. 2021
Wackersdorf, GER
Embedded Software development, Electronics and PCB design, test automation.

Education

Bachelor of Science
Oct. 2024 - Unknown
FernUniversität in Hagen, Hagen GER
Program: Mathematics
Master of Science in Engineering
Sept. 2021 - Mar. 2024
Paris Lodron University, Salzburg AT
Program: Applied Image and Signal Processing
Focus: Data Science and Machine Learning
Thesis: "Topology Preserving Contrastive Learning", Grade: 1.0
Overall Grade: 1.57
Bachelor of Engineering
Mar. 2017 - Apr. 2021
University of Applied Sciences, Regensburg GER
Program: Electrical Engineering and Information Technology
Focus: Embedded Systems Engineering.
Thesis: "Comparative Analysis of Nonlinear Optimization Algorithms for Optimal Control of Galvanometer-Mirror Systems", Grade: 1.0
Overall Grade: 1.8
Abitur
Sept. 2008 - July 2016
Johann Andreas Schmeller Gymnasium, Nabburg GER
Natural Science Track (Focus on MINT)
Overall Grade: 2.0