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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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, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics
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with several PhD students, postdocs and staff members. You will be working on a combination of topics in a collaborative environment, including cell biology, muscle physiology and exercise physiology
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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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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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://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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, fabricating, and characterizing effective memristive devices, with the goal of forming device networks for realizing the physical learning paradigm developed at AMOLF. You will integrate these memristive