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Role Description 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 fellow will conduct research on Algorithmic Verification of Concurrent Systems...
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We are searching for a skilled and experienced post doc with specific interest in investigating early myeloid disease, clonal haematopoiesis, metabolism and obesity. Our research/The group The focus
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technologies Research Field Computer science » Computer systems Computer science » Computer hardware Researcher Profile First Stage Researcher (R1) Application Deadline 11 Aug 2026 - 21:59 (UTC) Country Denmark
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reduction, system identification, power electronics, model predictive control, multi-objective optimization, machine learning, renewable-energy integration, experimental testing, or hardware-in-the-loop
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Are you interested in shaping the future of intelligent and secure computing systems where artificial intelligence meets reliable embedded hardware? We are looking for a motivated PhD candidate, as
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dynamics and drug responses. The candidate will use atomistic MD simulations to identify biophysical descriptors of receptor function and integrate these with machine-learning approaches to predict ligand
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responsible for: Conducting research on sustainable and resource-efficient AI systems for edge datacentres. Creating hardware-aware search spaces for CPUs, GPUs, and accelerators, and developing multi-objective
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, experimental testing, or hardware-in-the-loop implementation would be an advantage. You are not expected to have expertise in all of these areas. Qualification requirements PhD stipends are allocated
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assistant professors), 7 administrative staff, and 30 members of staff who are hired on temporary contracts as research assistants, post docs or PhD students. The department puts substantial emphasis
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are continuously subject to "domain shifts" caused by fluctuating conditions, hardware degradation, or changing physical surroundings. Traditional AI models are often brittle under these distribution shifts, leading