89 engineering-computation-"https:"-"https:"-"https:"-"https:"-"https:"-"BioData"-"BioData" positions at Aarhus University
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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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Would you like to facilitate open collaborations that lead to more sustainable and plant-based foods? We are looking for an experienced programme manager for the Plant2Food programme that has been
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and technology comprehension tools for children investigating children’s understandings and conceptualisations of computational and AI-based systems designing and empirically studying learning
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trapped ion quantum technology setups. The work will partly be carried out in the newly established Quantum Technology Lab (QTL) and within the Ion Trap Group. The position is open from the 1st of July
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scientific areas. We educate both Bachelor and Master of Science in Engineering and around 825 students are enrolled in our study programs. Furthermore, we also offer an ambitious PhD program. Our PhD students
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The Department of Biomedicine, Faculty of Health, Aarhus University invites applications for a postdoctoral position in Computational Spatial Proteomics and Multi-Omics Integration, starting 1
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We invite applications from researchers to join our XR (extended reality) and Visual Computing research groups. We have 3 posts available from 1 May but there is flexibility for later start dates
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at all levels, including PhD students. Our study programme portfolio in engineering consists of an MSc in Engineering (Technology Based Business Development) as well as two Bachelor of Engineering
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next‑generation insect camera traps with on‑device (edge) computing for real‑time detection and classification Collaborating in an interdisciplinary team spanning ecology, computer science, engineering
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at the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum