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on cybersecurity, embedded systems, AI, and IoT, with a focus on building secure, energy-efficient, and intelligent computing architectures. The two departments host an international community of researchers and PhD
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intelligence within grid-connected power converters and variable-frequency motor drives with edge computing and machine learning capabilities. We offer a multidisciplinary, international, and friendly atmosphere
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qualifications In this project, we advance an AI-driven T cell-targeting strategy based on de novo-designed peptide-MHC (pMHC) binding proteins. Using computational protein design, we create synthetic recognition
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Postdoc position in method development in human statistical genetics, with a focus on classificat...
understanding genetic architecture). Qualifications and specific competences The postdoc position focuses on developing statistical methods, therefore the candidates must be able to demonstrate they have skills
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medicine. The lab invented the RNA origami method that allows rational design of RNA scaffolds and devices for expression in cells [1] and have recently developed RNA origami robots that can sense, compute
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medicine. The lab invented the RNA origami method that allows rational design of RNA scaffolds and devices for expression in cells [1] and have recently developed RNA origami robots that can sense, compute
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Job Description These days, the inner workings of molecules and materials can be probed and modelled by advanced simulation tools on modern computer architectures. However, the routine applications
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. 3) Data-Driven Decision Support & AI-Enabled Operations The candidate conducts research on digital architectures and data-driven decision support for operations planning, system configuration, and
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understanding genetic architecture). Qualifications and specific competences The postdoc position focuses on developing statistical methods, therefore the candidates must be able to demonstrate they have skills
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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