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of computer graphics fundamentals, numerical methods, and GPU/parallel computing concepts. Experience with at least one major deep learning framework (PyTorch preferred). Excellent problem-solving skills and
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using GPU-based software packages, custom-trained neural networks, and related tools. Analyze and interpret high-dimensional neural datasets using systems neuroscience approaches such as neural networks
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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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GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer vision challenges) Experience with version control (Git) and collaborative development practices
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modules, and monitor training progress. Display performance metrics (e.g., inference time, GPU utilization, throughput, ROI impact) in real time. System Integration Work with the research team to connect AI
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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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the necessary algorithms. You will also develop and document a graphical user interface which handles large processing tasks efficiently and uses multiprocessing and GPU acceleration where necessary. At the same
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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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these resources through a cloud-native Kubernetes environment integrating large-scale CPU and GPU resources, Ceph object storage, BinderHub, Coffea-Casa, Dask, and ServiceX. This platform supports more than 500
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interface which handles large processing tasks efficiently and uses multiprocessing and GPU acceleration where necessary. At the same time, the software must be lean enough to run not only on powerful