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Field
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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
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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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automated data pipelines for large-scale time-series or imaging datasets. Experience with HPC/cluster computing environments, including SLURM job scheduling and GPU-accelerated processing. Experience with
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). The applicant’s experience must be related to the tasks to be conducted, to the job description herein or to: · Training of multimodal models based on deep learning and GPU computing. · Evaluation and deployment
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computing platforms, including Jetson AGX Orin, Jetson Thor, or comparable GPU-based robotic compute systems. Experience building, modifying, or troubleshooting Linux device drivers or integrating vendor SDKs
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such as TensorFlow, PyTorch, or JAX is a must and experience with GPU-based experimentation and cluster computing (e.g., Docker, Slurm) a plus. In addition to the above, there is also a mandatory
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modules, AWS, Azure, GCP, or comparable services. Experience supporting GPU, AI/ML, CPU-intensive, statistical, imaging, compiler/toolchain, Fortran, MATLAB, R, Python, CUDA, or other discipline-specific
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resources. Working knowledge of NVIDIA-specific technologies relevant to research computing, such as GPU-enabled workflows, CUDA-enabled tools, GPU resource use, or AI/ML computing environments. Basic
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methods on accelerated or heterogeneous computing platforms, including GPUs, FPGAs or other specialised hardware, for real-time or large-scale physics applications. We welcome candidates whose work is
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REQUIREMENTS The research assistant will be conducting research on GPUs, evaluating scheduling mechanisms to boost GPU utilization. BACKGROUND CHECKS/CLEARANCES Employment with the University will require