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years as part of a third-party funded project and to be filled with 80-100 % of the regular weekly working hours. Your Profile Master’s degree in Computer Science, Bioinformatics, Data Science, or a
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and GPU-accelerated computing. Familiarity with job scheduling (e.g. SLURM), conda/mamba environment management, and pipeline orchestration (e.g. Snakemake) is required. have strong proficiency in
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the advent of 5G/6G networks, the Internet of Things (IoT), and edge computing. This evolution enables services to be deployed across the edge-network-cloud continuum [1], leveraging heterogeneous
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critical role in defining the future of research and administrative computing at the University. In this role, you will lead the architectural design and deployment of secure, scalable AI platforms
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Fundación para la Investigación Biomédica del Hospital Gregorio Marañón (FIBHGM) | Spain | 17 days ago
of computing clusters and/or GPUs, as well as basic knowledge of computational fluid dynamics or numerical simulation, will also be valued. The ability to integrate computational tools with real clinical data
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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and
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well as large-scale GPU computing facilities for deep learning. Our Lab aims to hire a Research Associate to lead a research project on Real-World Deepfake Detection and Image Forgery Localization. The role will
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development skills Model deployment (e.g., ONNX, TensorRT) Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D
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-performance computing systems, GPU acceleration, and parallel file systems - Ability to communicate fluently in English, both spoken and written Additional qualifications - Knowledge of or interest in
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(URCF) at Drexel University is building a new shared computing platform focused on GPU-accelerated workloads, particularly AI model training. The system includes GPU and CPU compute nodes with Nvidia H200