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Field
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(especially proof assistants), GPU programming, low-level systems engineering in languages such as C/C++/Rust, systems for ML. Experience in writing academic papers. Please see job description for a full list
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, SimpleITK, MONAI, or nibabel), knowledge of Linux, Git, virtual environments and containers (Docker), and experience in training models on GPU in secure environments are required. Languages: Oral and written
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with AMD MI300A GPU+CPU. 2. Perform benchmarking studies to enhance scalability and achieve high node-level efficiency, surpassing existing AMR frameworks. 3. Contribute to communication optimizations
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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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Programming Interfaces), high-performance computing cluster, Bash or GPU programming is preferred. When submitting your application, please list your programming experience, including your depth of knowledge
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-learning architectures for sequential data (e.g., Transformers, graph neural networks, state-space models). Experience with OpenCV, GPU-accelerated inference, Docker, and modern software engineering
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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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team and supported by cutting-edge HPC and GPU infrastructure, you will contribute to internationally leading research, publish in high-impact journals and present your work at major scientific
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large GPU clusters on cryoSTEM datasets in the multi-terabyte range. This position plays a pivotal role in supporting ongoing, high-impact research programs within our lab. The successful candidate will
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Nextflow or Snakemake, version control such as Git, and reproducible computational environments is preferred. Familiarity with GPU-accelerated genomics, high-performance computing, or cloud-based analysis