Wilhelm Kirchgässner
Data Scientist
Greater Bielefeld Area Germany
Summary
👤
Senior🎓
Top SchoolWilhelm Kirchgässner is a data scientist specializing in machine learning for electrical drive and motion control systems, with nine years of experience bridging research and industrial deployment. Currently at Beckhoff Automation, he applies ML and data engineering to XPlanar position feedback, building on a doctoral-level background in electrical engineering from Universität Paderborn. His work ranges from thermal and control modeling of permanent magnet synchronous motors to reinforcement-learning–based current control, and he contributed Monte Carlo RL material and example control trajectories for a popular university course repository. Comfortable in Matlab and Python, he brings embedded software experience from Continental and a practical electronics apprenticeship, which gives him a rare combination of hands-on hardware insight and advanced ML research. Active on GitHub, Kaggle and ResearchGate, he translates academic rigor into production-ready solutions for motion control challenges.
9 years of coding experience
8 years of employment as a software developer
Dr.-Ing., Electrical Engineering, Summa cum laude, Dr.-Ing., Electrical Engineering, Summa cum laude at Universität PaderbornBachelor of Science (B.Sc.), Elektrotechnik, 1.3, Bachelor of Science (B.Sc.), Elektrotechnik, 1.3 at Hochschule Ostwestfalen-LippeElectrical Computer Engineering, Electrical Computer Engineering at University of California, Santa BarbaraGesellenbrief Elektroniker für Geräte und Systeme, Duales Studium, Gesellenbrief Elektroniker für Geräte und Systeme, Duales Studium at Phoenix Contact GmbH & Co. KG
deutsch (muttersprache), englisch (verhandlungssicher), russisch (grundkenntnisse)
Github Skills (7)
matlab10
reinforcement-learning10
machine-learning9
controlling8
jupyter-notebook7
user-manual6
python5
Programming languages (8)
Github contributions (5)
upb-lea/reinforcement_learning_course_materials
Jul 2020 - Jun 2022
Lecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University
Role in this project:
ML Engineer
ML EngineerContributions:2 releases, 1 review, 120 commits in 1 year 11 months
Contributions summary:Wilhelm's contributions primarily focus on implementing and refining a Monte Carlo reinforcement learning algorithm for the forest tree example. They developed and integrated code, including a Matlab script for calculating exemplary control trajectories, for the reinforcement learning course materials. The user's work involved visualizing results and creating corresponding plots.
noteshostedreinforcement-learningvideosdeep-reinforcement-learning
upb-lea/deep-pmsm
Oct 2019 - Jun 2021
Estimate intrinsic Permanent Magnet Synchronous Motor temperatures with deep recurrent and convolutional neural networks.
Contributions:6 commits, 6 pushes in 1 year 8 months
temperaturesdtwpermanent-magnetdeep-learningbackpropagation
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