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Field
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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in the fields of machine learning, epidemiology, big data analysis, and suicide and self-harm. Your work will expand the frontier of machine learning applications in epidemiology and improve our
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self-harm rates. You will expand your data wrangling, analytical and programming skills on python and develop expertise in the fields of machine learning, epidemiology, big data analysis, and suicide and
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using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts generated in the scattering
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The Centre for Machine Learning located under the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark invites
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training dataset of well-studied volcanoes with known large eruptions, the project will employ statistical and machine learning (ML) methods to identify the strongest predictors of eruption magnitude
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intensity of these changes. This PhD project will ultimately enable aircraft to reroute safely and efficiently in real time as weather evolves. By merging scientific machine learning, large-scale data
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workplace You will be employed in the Reasoning and Learning Lab (ReaL), Division of Artificial Intelligence and Integrated Computer Systems (AIICS) at the Department of Computer and Information Science (IDA
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of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts
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models, multi-view computer vision, semantic graph-based representations, and self-supervised learning—to automatically interpret and understand complex surgical procedures. The overarching goal is to