145 engineering-computation-"https:" "https:" "https:" "https:" "https:" "University of Minho" positions at Aarhus University
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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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design. However, there is a great need to develop new software for the design of advanced RNA origami robots that can sense, compute and actuate [2]. In the recently funded RIBOTICS (RNA Origami Technology
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stage and a more independent position. A core task in the position is to teach research-based courses in the history of science and technology within the BA and MA program in the history of ideas
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also offers a Junior Researcher Development Programme targeted at career development for postdocs at AU. You can read more about it here: http://talent.au.dk/junior-researcher-development-programme
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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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origami robots that can sense, compute and actuate [2]. In the recently funded RIBOTICS (RNA Origami Technology in Cell Systems) project, the lab aims to develop RNA origami robots for cell factories (yeast
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strategy and working environment is viewed as important. The Department is responsible for a Master’s programme in Molecular Nutrition and Food Technology, taught at AU, Denmark, and a Master’s programme in
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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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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