Johannes Schuck is a machine learning engineer with 11 years of experience specializing in computer vision and embedded edge deployments, currently building deep-learning patient monitoring and fall-detection systems in Oslo. He combines hands-on expertise in Python/PyTorch, C++/ARM inference (tflite/ONNX), and camera calibration with practical MLOps and tooling for dataset creation and annotation. Johannes has led vision projects from sensor and optics selection through raytraced simulation to deployed products—developing novel tracking and fiducial marker solutions for gaming and aquaculture applications. His background includes contact-less respiration and sleep monitoring research, giving him a strong foundation in signal processing and RGB-D sensor fusion that he translates into robust embedded solutions. Colleagues rely on him for rapid prototyping using 3D printing and synthetic datasets, and for cutting development time with pragmatic simulation-driven design choices.
11 years of coding experience
8 years of employment as a software developer
Information Technology, Information Technology at Karunya Institute of Technology and Sciences
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at Karlsruher Institut für Technologie (KIT)
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Contributions:24 pushes in 5 months
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Johannes Schuck - Machine Learning Engineer at Sensio