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). Detailed Curriculum Vitae, describing research, publication and teaching experience, computational skills and including personal contact information. Verified copy of the official Master's and PhD degree
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for computational biology and a track record of excellence in graph machine learning and multi-omics data integration? Look no further – an exciting Postdoc opportunity awaits you at the Novo Nordisk
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Denmark’s distinctively rich registry data, exploring a range of life outcomes throughout early development on the full Danish population (including on crucial subsets such as adoptees). We seek to use
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the project. Qualified candidates should have: A PhD degree in Computer Science, Electrical Engineering or equivalent. Research interests and a scientific track record in Edge Computing research fields, such as
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Postdoc in assessing carbon sequestration potential of different wetlands as nature-based solutio...
five bioclimatic zones. Your task will be to combine multi-source data — including field observations, environmental measurements, and satellite imagery — with mechanistic models to assess carbon
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five bioclimatic zones. Your task will be to combine multi-source data — including field observations, environmental measurements, and satellite imagery — with mechanistic models to assess carbon
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in the face of climate change. You will: Conduct ecological fieldwork at research sites in Sweden and Denmark. Collect and analyse phenological data using camera and drone-based imagery. Use genetic
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-supervise MSc and PhD students. Optionally support teaching and proposal activities. Required qualifications: As a formal qualification, you must hold a PhD degree (or equivalent) in computer science
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will commence in collaboration with a dedicated team of experienced researchers, lab engineers, and PhD students associated with the project, and in collaboration with researchers at partner university
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contribute to development of research grant applications. Your profile The applicants should hold a PhD in structural dynamics with focus on data-driven methods (e.g., for input/state/parameter estimation) and