Index
RPA-Bots zur Prozessautomatisierung im Workflow Management der DATEV eG
Advanced Machine Learning Models for Leakage Detection and Localization in Water Distribution Networks Using Real-System Data
Reinforcement Learning for Centralized Fault Coordination in Power Systems
In this project, we develop a hybrid reinforcement learning framework for adaptive protection in power grids with high DER penetration. A centralized model is first trained using system-wide current, voltage, and impedance data to coordinate both primary and backup relays, followed by decentralized fine-tuning using only local measurements to ensure autonomous operation in case of communication loss. The approach aims to improve relay coordination, robustness, and decision-making by exploring different recurrent network architectures such as RNN and LSTM.
Latent Space Modeling for Event Detection in Power Grid Data
This project explores how latent representations learned from raw grid waveforms can reveal underlying structure and enable early detection of abnormal events. By modeling high-frequency voltage and current signals, we aim to distinguish critical disturbances from normal behavior with minimal delay.
Report Generation in pathology using WSIs
This project focuses on developing methods for processing large-scale digital pathology datasets and extracting meaningful features from whole slide images to support automated report generation. Emphasis is placed on efficient handling of gigapixel image data and preparing it for use in vision-language models for clinical applications.
Advancing Lung Imaging Assessment in Nuclear Medicine
Master Thesis on Advancing Lung Imaging Assessment in Nuclear Medicine
Molecular Imaging of lung ventilation and perfusion allows functional assessment that is clinically useful for managing pulmonary diseases. This project focuses on developing and evaluating new methods for the advanced visualization and automated quantification of three-dimensional lung imaging in nuclear medicine.
Your profile and skills:
- You have programming proficiency with Python
- Familiarity with medical imaging and image processing is a plus
- You work analytically, in a structured and quality-conscious manner
- You are able to work independently and enjoy a collaborative team environment
- You have excellent communication skills in English
The thesis is planned to begin start of September 2025
Please send your transcript of records, CV, and a small motivation letter on why you would be interested in the topic to maximilian.reymann@fau.de