PhD position in physics-based machine learning modeling

- development of novel machine models based on supervised, unsupervised and reinforcement learning that can be combined with, enhance or replace methods from computational engineering and computer simulation - data modeling and assimilation towards experimental measurements under consideration of uncertainties - utilization of Explainable AI techniques to enable novel scientific discoveries - explore and evaluate usability of system architectures such as Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference - implementation of your machine learning pipeline in Python (using e.g. PyTorch) - validation of your results in collaboration with colleagues from various application areas (cross-disciplinary) - publication and presentation of your scientific results in international scientific journals and at international conferences and workshops