67 parallel-processing-"https:" Postdoctoral research jobs at Aarhus University in Denmark
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nicolaivoneggers@cas.au.dk For further information about the application procedure, please contact HR supporter Gerd Bech Thomsen (gebeth@au.dk ) The work environment Researchers at the Department of History work
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fieldwork, processing and analysing empirical material, and organising seminars and meetings with the advisory board. In addition to conducting and completing the subproject, the postdoctoral researcher is
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processes and nanomaterial functionalization. Comprising around 20 members - including PhD students, postdocs, and technical staff - the group fosters a collaborative, interdisciplinary environment focused
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University, Department of Biological and Chemical Engineering (AU-BCE) encompasses some 200+ employees and five educations. Position is embedded in the section for Process & Materials Engineering, where
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collaboration with the project team take an active part in developing and fulfilling the different processes, meetings and events related to the project in close collaboration with the project team Qualifications
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. The postdoctoral researcher will collaborate closely with an engineering team responsible for process integration and prototype development Expected start date and duration of employment This is a 2.5–year position
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research profile within organisational studies, Computer-Supported Cooperative Work, Human-Computer Interaction or related research areas as documented by a PhD dissertation and/or research publications
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processes. You will work experimentally with already established experiments including lysimeter trials, and you will have the opportunity to design and initiate new experiments. We expect that you will be
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Application procedure Shortlisting is used. This means that after the deadline for applications – and with the assistance from the assessment committee chairman, and the appointment committee
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decomposed into modular sub-components that can be either process-based models and/or deep learning models. MCL has the flexibility to replace any uncertain process description with a deep learning model