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, production-grade pipeline encompassing scalable video preprocessing, model training, and inference workflows. Implement GPU-accelerated training and inference, standardized evaluation protocols, and
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or research experience in computer vision or machine learning. • Experience optimizing models for real-time inference on edge and embedded platforms using techniques such as quantization, pruning, and GPU
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of the following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high-performance computing
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Zurich or another Swiss university Proven experience in software development Strong Python programming skills, with demonstrable experience in several of the following areas: GUI development CUDA for GPU
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roles or positions in industry. A supportive and collaborative team committed to fostering your growth as a researcher. What You’ll Do Process calcium imaging data using GPU-based software packages
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, implementing input sanitization, and contributing to AI‑safety research. Utilizing GPU/TPU resources, mixed‑precision training, and distributed training frameworks such as DeepSpeed or ZeRO. Prior work
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include: Assist in porting quantum computing optimization codes to CUDA-Q for use with NVIDIA GPUs. Collaborate with NLR researchers on developing formulations and algorithms for solving optimization under
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members in designing and integrating solutions into the AI(X) compute, software and data infrastructure stack, hardening these solutions, testing these on modern high-performance GPU compute clusters, and
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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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Infrastructure - Collaborate with research computing teams and HPC centers to architect AI infrastructure solutions that support both administrative and research computing needs, including GPU-accelerated