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crystalline materials is often responsible for improved materials performance, but it is commonly overlooked in computational materials design. In this project, we will focus on developing accelerated
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dynamics (CFD) will be developed and coupled with the existing model to account for melt pool dynamics to include advection in the thermal modeling and to predict porosities and imperfection trajectories in
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of Environmental Samples is open for appointment from 1 June 2026 or soon hereafter. Your work tasks The Senior Researcher is expected to maintain and develop the high-throughput DNA sequencing laboratory
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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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Postdoc – Performance requirements for biobased construction materials used in the building envelope
existing materials as well as new products, covering both new buildings and renovation. Focusing on developing robust solutions that enhance the sustainability of construction, including increased use
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the formation of anaerobic biofilms and granules, and on developing novel solutions to promote biofilm formation. The research work will have a cross-disciplinary nature and will involve a range of
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the opportunity to explore and develop the academic intersections where new insights and solutions emerge. Read more about our vision here. About the department FKF is one of four departments
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Job Description The University of Southern Denmark in Odense is looking for a student in computer science / data science / mathematics or comparable fields to assist us in development work for our
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solutions. This position centers on strategic guidance for innovation activities, intellectual property development, and the creation of viable business models that support technology commercialization. A
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for screening purposes and cell-based therapies. We will develop methods for modelling missing not at random (MNAR) observations and quantifying uncertainty using Bayesian methods and deep learning architectures