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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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manager or area developer. Transitions should therefore not be governed in isolation but collectively. Stakeholders, however, often hold their own individual values and interests. Think, for example, of a
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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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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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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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mechanisms during DNA replication. By integrating defined DNA templates, biochemical assays, and mass spectrometry, we will uncover how these structures are detected and processed, and what consequences