65 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Dr" "UCL" Postdoctoral positions at Nature Careers in Denmark
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
activities are project-based with a solid tradition in cross-disciplinary research and international collaboration. Job description The postdoctoral researcher will work with Dr. Robert Ladwig on developing
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departments. Contact information For further information, please contact: Dr., Peter Zeller, peter.zeller@mbg.au.dk Deadline Applications must be received no later than 23 February 2026. Application procedure
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part of the research groups of Drs. Lars Angenent and Alfred Spormann in a joint collaborative project, which also includes Prof. Bruce Logan of Penn State University. We are seeking highly talented and
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: Establish and develop experimental protocols and pipelines and implement data management compliance. Presentation of your work in various meetings (locally at the department, national and international
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. Development of quantitative image analysis to extract information on atomic vibrations and displacements from image series. Investigate molecular adsorption, surface reconstruction and site-dependent reactivity
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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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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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sections with around 350 highly skilled employees, of which approximately 50% are scientific staff. More information can be found here . We believe in encouraging inclusion, acceptance, and understanding by
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researcher network. The department consists of nine research sections with around 350 highly skilled employees, of which approximately 50% are scientific staff. More information can be found here . We believe