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, POPL), verification (e.g. CAV) or top venues in closely related fields. Experience in at least one of the following (ideally more than one): formal verification tools (especially proof assistants), GPU
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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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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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commercial sponsors. A particular strength is real-time RF system development leveraging in-house FPGA, GPU, and SDR platforms. Importantly, SDD has successfully transitioned multiple capabilities for real
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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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the thermal process from GPU computation to radiative cooling Integrate the algorithms into a larger computational satellite simulation environment Identify new cyber-physical systems research topics related
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-related discipline. 2) Strong programming ability in optimisation or machine learning (e.g., Python/Matlab/C++; PyTorch/TensorFlow). Experience in signal processing/wireless or SDR/GPU prototyping is a plus
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to gather requirements, manage expectations, and balance competing priorities. Experience supporting high-performance or GPU-enabled scientific computing environments, including NVIDIA/CUDA, performance
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Karel. He's an AI agent we built, and he writes like a monk keeping vigil over a datacenter. Every morning Karel files a report: which pods are healthy, which GPUs sit idle, which tickets are aging, where