281 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "University of London" Postdoctoral positions at Nature Careers
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-EM sample preparation, data collection, and high-resolution structure determination • Process and refine complex cryo-EM datasets using strong computational skills (RELION, cryoSPARC, etc.) • Conduct
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moss will be harvested with the purpose of sphagnum restoration at other sites or as growing media in horticultural production. The main focus of your position will be chamber measurements and data
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datasets, UAV-derived information on NBS, and environmental data to quantify ecosystem services across Danish agricultural landscapes. The postdoc will work within an interdisciplinary team of applied and
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50% are scientific staff. More information can be found here . We believe in encouraging inclusion, acceptance, and understanding by employing staff who bring unique perspectives to our department
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information For further information, please contact: Professor Daniel Otzen, dao@inano.au.dk Application procedure Shortlisting is used. This means that after the deadline for applications – and with
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perturbation prediction, including the design and implementation of novel training strategies under experimental constraints, e.g., active learning and other data-efficient approachesConduct large-scale
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microscopy data analysis, chemometrics, and machine learning. This position is ideal for a researcher who enjoys working at the interface of imaging, data science, and environmental monitoring. The project
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dynamics information. As a postdoc, you will contribute to the development of single molecule fluorescence real-time imaging methodologies using both experimental approaches, involving model nucleic acids
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motivation to work on ultrafast spectroscopy, postdoctoral fellows should have an interest in working on biomedical problems. Contact Further information about the position may be obtained from Associate
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biopsy development. This role is suitable for candidates with either (i) a computational background (e.g. bioinformatics, data science, computational biology) who enjoy working closely with experimental