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Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position
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computational methods as the necessary foundation to venturing and expanding the field through modern approaches in machine learning and artificial intelligence. The appointment is hosted by the Department
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Applicants are invited for a PhD Fellowship/Scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Civil and Architectural Engineering programme. The position
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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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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
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of electrolyzer technologies, digital twins, model order reduction, system identification, power electronics, model predictive control, multi-objective optimization, machine learning, renewable-energy integration
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of Health Science and Technology, one or more PhD stipends in Unsupervised Learning for Medical Image Analysis are available for appointment from November 1, 2026, or as soon as possible thereafter
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records commensurate with grade Desirable criteria Ability to carry out statistical analysis of genetic data Up to date knowledge of machine learning methods applied to clinical and omics data Experience in
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intelligence and machine learning, with areas such as enzyme discovery and engineering, metabolic pathway design, genetic-code engineering, protein engineering, and the biosynthesis or biological incorporation