18 parallel-computing-numerical-methods-"Multiple" Postdoctoral positions at Aarhus University in Denmark
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The Department of Media and Journalism Studies within the School of Communication and Culture at Aarhus University invites applications for a postdoctoral position in digital methods and the creator
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The Section for Electrical Energy Technology at the Department of Electrical and Computer Engineering (ECE), Aarhus University, is in a phase of rapid growth in both education and research
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graph algorithms for optimization under physical constraints Applying graph mining and graph data management techniques Designing computational methods for waste heat reuse and green transition goals
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This is a full-time (37 hours/week) on-site role located at Åbogade 34, 8200 Aarhus N, Denmark for a Postdoctoral Fellow at the Department of Computer Science, Aarhus University. The postdoctoral
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mission enabler programme under CORC, where other modelling activities will run in parallel to support and be supported by the findings of the digital twin solution. This innovative ambition is shared with
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-cell level, even in low-cell-number CSF samples. Such a method would enable high-resolution immune profiling with potential application to diseases like multiple sclerosis (MS), Parkinson’s disease (PD
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Are you interested in real-time distributed systems, IoT connectivity, and AI-driven automation? The Department of Electrical and Computer Engineering at Aarhus University invites applications for a
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Documentation of any teaching experience The successful applicant will be expected to: Conduct analyses pertaining to radiocarbon dating on multiple types of materials, though predominantly human and animal bone
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with experimental or computational methods for studying the failure of complex and heterogeneous materials. You have demonstrated the ability to publish in high-quality international journals and to
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across molecular biology, ecology, bioinformatics, and environmental science. The taxonomic scope is broad and inclusive: we aim to collect comprehensive data across multiple taxonomic groups to support a