27 phd-in-architecture-and-built-environment-"Prof" Postdoctoral positions at Aarhus University
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; Collaborating closely with the ENGREENIT’s PhD candidate (starting 12 months later than the PostDoc researcher), supervised by the Assoc. Prof. Emil Dražević, and will jointly develop the heterogeneous
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an established team that studies chemical pollution in the environment as well as human exposure to these chemicals . In addition to high-quality accredited analyses and target screening approaches, the group has
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. The working environment is very collaborative and PhD students and postdocs are encouraged to develop they own independent projects Place of work and area of employment The place of work is Bioinformatics
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two PhD students in the groups, and your newly developed tools will support the analysis and interpretation of their data. You will interact strongly with several international collaborators, both
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our BSc. and MSc. program by contributing with teaching and supervision? Then the Department of Civil and Architectural Engineering invites you to apply for a 4-year postdoc position on position “data
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computational humanities audiences. Qualifications Applicants should: Hold a PhD or equivalent qualifications in computational history, computational archaeology, computational social science, cultural evolution
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evaluation process etc please visit: ambercofund.eu Qualification requirements Minimum requirements are: candidate needs to have a maximum of 8 years after a doctoral degree (PhD), as required by the project
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July 2026 and in other dissemination activities. Qualifications Applicants must have a PhD degree or must document equivalent qualifications in a relevant field related to Old Norse studies, for instance
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multidisciplinary environment at the Faculty of Health. You will report to the Head of Section for Oral and Maxillofacial Surgery and Oral Pathology, Kasia Gurzawska-Comis. Your competences You hold a PhD in a field
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engaging research environment. The postdocs will work on cultural alignment and preference optimization of large language models (LLMs) for mid to low-resourced languages. The overarching goal is to develop