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and development in the field of software engineering for dependable and safe automated driving systems. Conception, design, and implementation of software architectures and prototypes in
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support of data/metadata formats developed in the group Integration of the developed software into the computational environments and data handling routines Presentation of the scientific results
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to the scattering data analysis and support of data/metadata formats developed in the group Integration of the developed software into the computational environments and data handling routines Presentation
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expertise in developing and training machine learning models (ideally with a focus on LLM), high-performance computing, data management, and software architecture Strong Python programming skills and
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-Angle X-ray Scattering (GIWAXS) and others) Contribution to the scattering data analysis and support of data/metadata formats developed in the group Integration of the developed software
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, CTR) Design and implement physics-informed deep neural network architectures which accelerate finding electron bunch shapes self-consistently for large measurement campaigns Implementation and
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applied Machine Learning Hands-on experience with High Performance Computing Systems Basic knowledge of System Architecture of Supercomputers and NVidia-GPUs Practical experience with ML/DL workflows and
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01.10.2025, Wissenschaftliches Personal This PhD position is part of the interdisciplinary TUM GNI project AUROrA – AI-Driven Urban Flood Resilience: Integrating Earth Observation and Architectural
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algorithms for application parallelization, simulators and virtual platforms for application- and architecture exploration, hardware/software co-design and operating/runtime systems. Typical application
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The research group Genetics and Physiology of Root Development studies crop root anatomical and architectural traits. Research is mainly focused on cereals including barley, wheat, and maize. We