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, or other highly regulated computing environments. HPC, AI/ML infrastructure, GPU computing, containers, Kubernetes, or advanced computing platforms. Infrastructure-as-code, advanced automation, CI/CD
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. This position will work within Research Computing to architect advanced computing, GPU-enabled environments and serve as a specialized partner to researchers to develop AI/ML research workflows within UAB
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Python and experience with GPU processing of large-scale datasets. Excellent written and oral communication skills in English. We also value applications from people with the following experience
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-on experience in one or more of the following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high
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datasets. Experience with GPU-accelerated computing or high-performance computing environments. Experience contributing to research proposals, supervising students or working within international
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. · Knowledge in servers with GPUs, and development of Artificial Intelligence applications. · Solid understanding of Pattern Recognition, Machine Learning, and Deep Learning with applications to health issues
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distributed training concepts, GPU-based training, checkpointing, experiment tracking, training stability and compute optimisation. MLOps, infrastructure and deployment: Familiarity with Linux environments
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or OpenMP. Experience in heterogeneous programming (i.e., GPU programming) and/or developing, debugging, and profiling massively parallel codes. Experience with using high performance computing for lattice
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practices and international standards. Leverage VIB Data Core services, including high-performance computing pipelines and large-scale GPU resources, to scale ML development and deployment. Your profile PhD
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scientific programming in Python and experience with GPU processing of large-scale datasets. Experience with inverse problems and 3D reconstruction methods for tomography, laminography, or a closely related