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Specific Integrated Circuits (ASIC), Processors (CPU, GPU, VPU and accelerators), Field Programmable Gate Arrays (FPGA) and System-on-Chips (SoCs), as well as Intellectual Property (IP) Core developments for
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or similar deep learning frameworks GPU know-how: Familiar with GPU workflows and distributed training setups Data competence: Experience in preprocessing, augmentation, and dataset organization; confident
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intelligence models (LLMs) in multi-GPU environments. Preparation of technical documentation, best practices for development and operation. Where to apply Website https://sede.uvigo.gal/public/catalog-detail
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Position Summary: The Research Engineer will be responsible for the smooth operation of the VIDAR Lab hardware and software stacks, including GPU clusters and related computing resources. This position will
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and generative AI solutions tailored for large-scale research datasets. Prototype AI applications on local GPU-hardware, ensuring seamless scalability to HPC environments like the Gefion supercomputer
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facilities. Core responsibilities include the deployment and maintenance of small-scale HPC and compute nodes, GPU workstations, Linux and Windows servers, research data storage and backup solutions
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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infrastructure, and GPU/CPU cluster environments. This role leads and mentors a team of Systems Engineers and Administrators while remaining deeply technical and hands-on, actively designing, deploying, and tuning
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platforms such as llm servers, shared virtual GPUs (VGPUs) used by OPS-G, and the broader utilization of cloud resources. Ensuring the smooth operation, availability, and continuous improvement
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optimization – with rigorous theoretical analysis. The ideal candidate has strong machine learning and AI expertise and is comfortable with – or eager to learn – large-scale multi-GPU experimentation