Tobias Schraut

Implementation of multi‑sensor biomedical datasets integrating acoustic recordings and laryngeal high‑speed imaging; Signal and image processing; Feature engineering and selection; Development and integration of machine/deep learning models; Maintenance and adjustment of the hardware–software integration of high‑speed imaging systems; Development and maintenance of clinical research software; Peer‑reviewed publications and presentations at international conferences;

Automatic end‑of‑line testing of electric motors based on acoustic signals and convolutional neural networks; Dynamic system identification of spring‑mass‑damper systems using recurrent neural networks; Reinforcement learning for adaptive PID control; Presentations on the principles and potential applications of machine learning and reinforcement learning;

Design and development of a machine learning‑based online clinical decision support system

Design and development of a machine learning‑based online clinical decision support system

Assembly, testing, and repair of electronic measuring, control, and regulation devices

Introduction to & hands‑on involvement in model construction, melting processes, and machining techniques

Final grade: 1.2 PhD thesis: Machine learning for multimodal assessment of hoarseness severity. DOI: 10.25593/open‑fau‑2349

Final grade: 1.6 Study course: Mechatronics Focus: Biomedical engineering Diploma thesis: End‑to‑end classification of sleep stages based on ECG signals (Grade: 1.0) Student thesis: Metric learning for clinical decision support systems (Grade: 1.1)