36 parallel-computing-"DIFFER" Postdoctoral positions at Aarhus University in Denmark
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modeling challenges focused on Quantum Circuits are encouraged to contact us with a CV and a short description of interests. The applicants must have a strong background in Theoretical Computer Science (e.g
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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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The Department of Computer Science, Aarhus University, invites applications for a full-time 2-year Postdoctoral position, starting 1 April 2026– or as soon as possible thereafter. Position and
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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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substantial knowledge and research experience in areas such as computational fluid dynamics, turbulence modeling, data-driven methodologies, machine learning, and parallel computing. The candidate should also
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lively, open and critical discussion within and across different fields of research a work environment with close working relationships, networking and social activities a workplace characterised by
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1840; namely France, Haiti, Greece, and Gran Colombia. The aim of the project is to understand how different conceptions of a republic and of political participation emerged in the period, and how
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wildfires and their impacts on both the stratosphere and climate combining different model systems. The position is to be filled by 1 May 2026 or as soon as possible thereafter. Expected start date and
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of Agroecology, our main goal is to contribute to sustainable solutions to some of the world’s biggest problems within the areas of soil, plants, animals, humans, and the environment. We want to make a difference
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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a postdoctoral position focused on deep learning based analysis of remote sensing data for groundwater