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proteins in the mixture together define the key properties of these systems. Predicting these properties by only studying their components might seem impossible... but that is what we aim to do in the Big
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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approaches such as network analysis (considered an advantage). Our offer The opportunity to work in and with a team of leading experts; a job for 4 years. After 18 months you will be reviewed. If positive
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project developers, need to deal with making implementation decisions in their daily activities as asset manager, investor, construction manager or area developer. Transitions should therefore not be
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reusable software and data artifacts where relevant. Communicate research outcomes through papers and talks at conferences, workshops, and beyond. Actively collaborate with other researchers in the TRL Lab
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-powered data analysis. Publish reusable software and data artifacts where relevant. Communicate research outcomes through papers and talks at conferences, workshops, and beyond. Actively collaborate with
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://romeijn.web.rug.nl/paper/2023_romeijn_-_VICI_project_description.pdf ), where you can find a detailed list of planned PhD and postdoc positions. Candidates can freely choose their research topic within the domain
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processes are important for the delivery of soil functions in these farms and how management practices shape these processes. Your work will help define potential soil indicators for success in the transition
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. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual annotations). The research will be conducted
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Administrative & facility Assistant professor Associate professor Education EngD Finance HRM IT Lecturer Legal staff Management Marketing and communications PhD Policy and staff PostDoc Professor Research