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2026, UTC will deploy a state-of-the-art scientific computing and storage infrastructure, comprising a high-memory, GPU-enabled HPC computing node and a modular, scalable RAID-based storage system
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staff to carry out activities related to research lines or scientific-technical services Ref. Interna M2832-7528 PID2021-122392OB-I00 Call for selection of research asistants https://sede.urjc.es/tablon
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inference Develop distributed model training and inference architectures leveraging GPU-based compute resources Implement server-less and containerized solutions using Docker, Kubernetes, and cloud-native
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degree in physics, mathematics or any related field; correspondingly, Postdocs hold a PhD or equivalent degree in the above mentioned fields. What we offer State of the art on-site high performance/GPU
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(static and dynamic photochemistry, heterogeneous catalysis, modeling of interfaces and ionic liquids). It benefits from access to the CBPSMN mesocenter, with a large amount CPUs and GPUs facilities. In
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samples. Optimize reconstruction algorithms for efficient large-scale 3D imaging, including high-performance and GPU-accelerated computing where appropriate. Design, optimize, and validate a refractive
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the development of scalable software tools and pipelines, potentially leveraging GPU/FPGA accelerators. Our aim is to build next-generation molecular atlases for chronic diseases and to improve patient
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-design with accelerators (FPGAs, GPUs, near-memory systems) to achieve real-time, energy-efficient AI for high-tech industry applications. Work with leading companies like ASMPT and shape the future of AI
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computing environments for advanced research. B2 Knowledge of scaling and optimising software to take advantage of GPU / HPC infrastructure. Skills Essential: C1 Excellent written and verbal communication
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research practices Experience training and deploying machine-learning models on GPU-based systems; familiarity with HPC environments is an advantage Interest in interdisciplinary research at the interface