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Field
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environments. Experience with parallel computing environments, HPC in a Linux environment. Experience with surrogate modeling. Experience with data analytics techniques. Familiarity with C++ and GPU programming
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). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI
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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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, forward-looking, and varied research fields and projects, with numerous development opportunities Modern hardware and infrastructure at the workplace, from compute and GPU servers to supercomputers
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/TimeSformer, CLIP/BLIP or similar) in PyTorch, including scalable training on GPUs and reproducible experimentation. Demonstrated experience building explainable models (e.g., concept bottlenecks, prototype
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disease insights. The lab has state-of-the-art computing capabilities with an in-house cluster serving 80 CPU cores and 1.5TB of RAM, as well as a newly acquired NVIDIA DGX box with eight H100 GPUs and 224
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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as
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. Programming & Software Development: Proficiency in Python, PyTorch, JAX, or other ML frameworks Computing: Some experience with large-scale datasets, parallel computing, and GPUs/TPUs. Algorithm Development
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E13) up to 5 years International collaboration to build a large radiotherapy dataset Dedicated GPU infrastructure Strong collaborations within TUM’s AI ecosystem High-impact publication potential
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managing supercomputer resources Strong skills in algorithm development for large sparse matrices Excellency in programming GPU accelerators from all major vendors Very good command of written and spoken