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Field
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novel machine-learning methodologies Excellent programming skills in Python and familiarity with modern ML tooling and reproducible research practices Experience training and deploying machine-learning
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: Advanced deep learning architectures Mathematical foundations of machine
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benefits, a wide variety of professional development opportunities, supportive work and family benefits, a wealth of health and wellness programs and resources, and much more. Posted Job Title Senior HPC
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thick and strongly scattering samples. Experience in high-performance computing, parallel programming, or GPU-accelerated computation for large-scale 3D reconstruction. Experience applying machine
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 8 days ago
across HPC, cloud and GPU environments, with proficiency in programming languages such as Python, Fortran, C, R or Julia and experience with modern AI/ML frameworks. They have expertise in data formats
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programme Access to secure clinical and multi-omics data environments Modern GPU, and high-performance computing resources, plus dedicated research-engineering support Close integration with clinicians and
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 16 hours ago
Program conducts regulatory science research to ensure safe and effective interoperable medical devices, including wearable technology and systems of devices in the home and integrated clinical environments
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 6 days ago
, environmental science or related fields. They demonstrate proven capability in developing, optimising and deploying scientific workflows across HPC, cloud and GPU environments, with proficiency in programming
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qualifications Experience with high-order or nonlinear scattering reconstruction methods for imaging thick and strongly scattering samples. Experience in high-performance computing, parallel programming, or GPU
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, PyTorch) for ML applications, training, evaluation, and deployment of models Use of GPU-based servers and modern IT infrastructure for training and inference Application of classical ML methods (e.g