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scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing predictive performance, generalisation
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. Professor Christoph Goebel (TUM) will act as the host at TUM. Possible topics of research include, but are not limited to • Exploiting GPUs for large-scale optimization • Value stacking of storage in
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candidates hold a Master’s degree in Informatics, Mathematics, or a related field, and possess strong expertise in linear algebra, GPU architectures, and programming in C++ and Python. This is a 100% TVL E13
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collation, mixed precision, multi-GPU; experiment tracking (W&B or MLflow), configuration management (Hydra), strict seed and artefact reproducibility. Graph machine learning. PyTorch Geometric or DGL
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of CPU and GPU resources to maximize model performance, scalability, and portability. A particular focus of the position will be a dedicated project to prepare, optimize, and benchmark the code
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baselines Solid Linux experience in production environments (RHEL/Almalinux/Ubuntu) Hands-on HPC background: Slurm, parallel file systems (Weka, Lustre, Ceph), GPU workloads and high-speed networks
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establish a research profile. Develop and execute innovative research projects. Develop, train, and evaluate modern machine-learning models on GPU/HPC infrastructure. Integrate AI methods with scientific
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architectures. Emphasis will be placed on heterogeneous computing and the optimal use of CPU and GPU resources to maximize model performance, scalability, and portability. A particular focus of the position will
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benchmark them with a realistic case study. The main focus of the project can develop either more in the mathematical theory of MCMC, the implementation of code for the Jülich supercomputers (GPU/CPU
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computing and GPU infrastructure for the development and evaluation of LLM-, VLM-, and agentic AI solutions Research across the entire automotive software development lifecycle, from requirements and software