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for candidates with experience in ML model deployment, workflow orchestration, and high-throughput data processing, as well as experience working with large biological datasets in scalable GPU-based computing
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datasets in scalable GPU-based computing environments. What we provide: A competitive compensation package, with comprehensive health and welfare benefits. A supportive team environment that promotes
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optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms Publish and present your results in peer-reviewed journals and at
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of visualisation, machine learning / AI, and human-computer interaction Very good programming skills (web-based visualisation, Python, and/or GPU programming) First experiences in the participation in research
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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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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 3 months ago
project (http://www.numpex.fr ) endowed with more than 40 million euros over 6 years from 2023, to build a software stack for Exascale supercomputers related to the arrival in Europe of the first Exascale
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capabilities. We can access a high-performance computer cluster with the most advanced GPU resources. We also partner with the New York Proton Center, which houses one cyclotron, three rotational gantry
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. Training LLMs, large-scale deep learning systems, and/or large foundation models using GPU/TPU parallelization while setting up the environment/system network under various constraints, such as limited
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hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a given Tiramisu program, many code optimizations should be applied
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and clinical MR systems fully dedicated to research, state-of-the-art local and scalable cloud-based compute infrastructure (CPU, GPU) and workshops for mechanical, electrical and electronic development