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Field
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models on one or more GPUs and the ability to work with existing codebases to set up training runs Research interest in one or more of the following areas: probabilistic machine learning, time series
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learning, multicore and GPU programming, and highly parallel systems. Good knowledge in one or more of the following programming languages/environments: C/C++, Python, PyTorch (or similar), and Cuda. Place
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of automation systems for software deployment, configuration management, CI/CD, and environment lifecycle processes. Partner with researchers and domain experts to optimize applications for CPU/GPU architectures
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- GPU. What we offer As well as the exciting opportunities this role presents, we also offer some great benefits some of which are below: 41 Days holiday (27 days annual leave 8 bank holiday and 6 closure
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communications and sensing, and a GPU Lab for training of advanced machine learning models. IDLab is both part of the University of Antwerp and the research centre imec. Position As a graduate teaching & research
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containerisation, orchestration platforms, and CI/CD practices. Benchmark, profile, and optimise AI applications across software and hardware layers to maximise GPU cluster efficiency Build research community
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- and hardware-oriented, reliable programming of parallel systems ranging from embedded multi- and many-core processors to large-scale heterogeneous systems (CPUs, GPUs, Accelerators, etc.). The research
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precision algorithms for CPUs and GPUs. Performance engineering and analysis including application profiling, benchmarking to identify performance bottlenecks. Verification, and validation of the developed
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influence the technological trajectory of the ecosystem. The core responsibilities of this position include developing and owning the overall SoC specifications and architecture, encompassing CPU, GPU, memory
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 2 months ago
methods (SFEM) offer superior accuracy per degree of freedom and are naturally suited to HPC architectures (CPU/GPU clusters). Two main Galerkin formulations exist: Continuous Galerkin (CG-SFEM): Memory