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well as large-scale GPU computing facilities for deep learning. We are looking for a Senior Research Engineer to manage the EEE GPU Cluster. The role will focus on leading the EEE GPU Cluster team and in charge
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FLAME-GPU accelerated agent-based modelling of material response to environmental and operational loading EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce
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languages; experience with GPU programming (e.g., CUDA) is highly desirable. Background in optimization, image-guided radiotherapy, medical imaging, or computational modeling. Experience with treatment
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 4 days ago
) Application Deadline 22 Mar 2026 - 00:00 (UTC) Country Australia Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is
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feet away from each other and work together to train models, architect systems, and run trading strategies. We work with petabytes of data, a computing cluster with hundreds of thousands of cores, and a
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. Describe a deep learning project you have executed. Projects in computer vision for microscopy image analysis are especially relevant. Include a link to a code repository if possible. If you contributed to a
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biology. Please include a cover letter with your application detailing your qualifications and experience for this position. Describe a deep learning project you have executed. Projects in computer vision
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learning architectures for scientific or high-performance computing applications. Background in software performance evaluation, profiling, and optimization on CPUs and GPUs. Knowledge of common numerical
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Information Benefits Trabajo en IA generativa de vanguardia aplicada al habla / Work on cutting-edge generative AI for speech Acceso a servidores GPU y recursos de cómputo / Access to GPU servers and computing
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3T Siemens MR scanners, OPM-MEG, EEG, eye tracking, and TMS laboratories. They will also have access to Princeton's world-class computational infrastructure, including GPU systems capable of running