172 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions in Denmark
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find contact persons at the bottom of the jobpost. Further information Read more about our recruitment process here The appointment process at Aalborg University involves a shortlisting process. You can
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large language models in distributed environments while safeguarding both model integrity and data confidentiality. The successful candidate will develop novel frameworks that enable effective
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paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported Collaborative Learning (CSCL), Computer-Supported
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, outreach and knowledge brokering activities of the Algorithms, Data and Democracy project (ADD) of which the IPA project is part (see www.algoritmer.org ). teach, supervise and examine students in Master of
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Aarhus C, and the area of employment is Aarhus University with affiliated institutions. Contact information For further information, please contact: Plant-PATH Center Coordinator, Leila Margot Henkes, +45
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machine learning to rich molecular data and uncover the molecular basis of IBD. The successful candidate will build prediction models from high-dimensional molecular measurements, with a particular focus on
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definerede – opgaver og processer. Det vil også være relevant at kunne støtte dem i anvendelsen af de i kurset anvendte software pakker (specielt Nvivo og Orange Data Mining – forudgående kendskab er en fordel
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part of the application. Contact information For further information, please contact Associate Professor Andreas Pavlogiannis at [email protected] Application procedure Shortlisting is used
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employment The place of work is Universitetsbyen 81, 8000 Aarhus C, Denmark, and the area of employment is Aarhus University with related departments. Contact information For further information, please
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methods for approaches such as model-data fusion techniques. The main focus of your position will be to develop and apply a scalable process-based modeling framework to evaluate climate-smart management