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are particularly relevant. The developed numerical models will leverage a large suite of in-situ and remotely sensed observational datasets, spanning from subsurface to atmosphere. You will have experience
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based on fossils. This will make it possible to pinpoint cognitive changes in geological time, which can be correlated with major events in the history of Earth. More information on our team can be found
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public health. In this role, you will develop and evaluate novel AI and machine learning methods using large-scale multimodal datasets, contributing to epidemiology-informed foundation models, predictive
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extreme weather forecasting, employing data science models as developed in computer vision and natural language processing (NLP). This project leverages the large-scale high-performance computing (HPC
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to manage, organize, and back up large amounts of image and tabular data generated by program and collaborator labs. Other Requirements: -May have to work on the weekend and/or after normal hours. -Travel is
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contribute to the advancement of knowledge in crystalluria. We have links with The Newcastle upon Tyne Hospital groups and with large groups based in the prestigious Bernal Institute in Ireland. This project
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The Section for Electrical Energy Technology at the Department of Electrical and Computer Engineering (ECE), Aarhus University, is in a phase of rapid growth in both education and research
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systems routinely handle data rates of several GB/s and support large-scale scientific data workflows. To support sustainable data management at next-generation photon science facilities, we are seeking a
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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Large Earthquakes”, led by a team of researchers at Durham and Oxford University in collaboration with international partners. The project builds on a multidisciplinary approach combining laboratory