82 data "https:" "https:" "https:" "https:" "I.E" "UCL" "UCL" Postdoctoral positions at Aarhus University
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quality and functioning, particularly in plumes near river outlets. This post doc project will rely on existing data as well as new field data of nutrients, carbon, and stable isotopes from riverine-coast
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cultural events including music festivals etc. See e.g. the recent recommendation by CNN (https://edition.cnn.com/travel/article/aarhus-denmark-things-to-do/index.html). Aarhus is easily reached through
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research collaborations, experience with survey data collection in- and outside Denmark will be an advantage as well as the ability to write and communicate fluently in Danish and English Experience with
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, Belgium, and Germany, and offers the successful candidate excellent opportunities for interdisciplinary training, exchange, and scientific collaboration. Plant-PATH homepage: https://mbg.au.dk/plant-path
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engaged scientific environment at the Section for Arctic Ecosystem Ecology (for more information see: https://ecos.au.dk/en/researchconsultancy/research-areas/arctic-ecosystem-ecology ). The department is
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., maternity/paternity leave), so we can subtract these periods from the span of their academic career when evaluating their productivity. For further information about the position, please contact
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well as interviews with the students and their families. Further information Applicants are encouraged to contact project manager Laura Gilliam (lagi@edu.au.dk) to obtain the full project description. The position
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on “ Integrating AI into Aquatic Ecosystem Models to Decode Ecological Complexity ” funded by Villum Fonden. Within that project, the focus is on exploring novel ways to infer information from environmental data
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reference If you want a referee to upload a letter of reference on your behalf, please state the referee’s contact information when you submit your application. We strongly recommend that you make
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implement state-of-the-art data science principles into dental practice. While our primary focus is on the use of deep learning in (dental) imaging, our work expands into any type of data (e.g. tabular data