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to reactor physics, computational methods, machine learning, and data science. Proficiency in modern software development practices, GIT-based version control, high performance computing platforms, and object
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machine learning techniques to develop emulators for the theoretical predictions of various observables as function of cosmological parameters. The candidate will develop and use skills in topics such as
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. Experience with data-driven modelling, machine learning, or AI applications in energy systems is an advantage. Familiarity with modelling of energy networks, district cooling systems, or integrated urban
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable
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recently funded centre of excellence (Integreat). Integreat collects scientists from statistics and computer science and offers a flourishing machine learning community, including many PhDs and PostDocs
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
machine-learning methods to investigate the deep-time controls on copper mineralisation. The role will involve developing reproducible computational workflows, generating predictive maps of copper
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samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue samples. Apply the developed
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experience with machine learning is a plus. Recruitment is open immediately and will continue until the position is filled. Applicants should send a brief statement of interest, CV, three named references, and
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of alumnus Dev Joneja, PhD ‘89. Maxwell Fellows provide a bridge to enhance collaboration among faculty developing cutting edge tools in data science and in biomedical research, potentially spanning
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of AI and in particular machine learning (ML). As today’s mainstream AI/ML workloads often resort to large-scale and energy-hungry supercomputers, it is necessary have a more critical look at how HPC