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the digital models into different simulation environments and to support the testing and training of medical robotic systems. The successful candidate should have a strong background in medical image analysis
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an experience in technology-assisted monitoring or computational image analysis. Expected start date and duration of employment The position will start in June 2026, with exact starting date as agreed between
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to prototype validation and measurement activities. Document design choices, trade-offs, and experimental results in high-quality publications. The position offers the opportunity to establish an independent
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hybrid models that integrate limnological knowledge into machine learning models following the paradigm of Knowledge-Guided Machine Learning (KGML). The position is part of an on-going project
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on understanding how different excitation methods generate polarons and correlated materials in the cuprates and other quantum materials, building on our recent results in the vanadium dioxide (see Johnson et al
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on understanding how different excitation methods generate polarons and correlated materials in the cuprates and other quantum materials, building on our recent results in the vanadium dioxide (see Johnson et al
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an expert in the extracellular vesicle field with skills in genetic engineering of extracellular vesicles (including transient/stable transgenesis of zebrafish), live embryo imaging, and spatial
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. Within the scope of the project, a small-scale prototype will be tested in AAU Energy’s state-of-the-art wave tank to characterise static and dynamic behaviour under wave and current loading
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-supervise MSc/PhD students and contribute to the dissemination of results through publications and conferences Qualifications As a formal qualification, you must hold a PhD degree (or equivalent) in
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, load modelling, soil–structure interaction, and relevant degradation and failure mechanisms over extended service periods. Within the scope of the project, a small-scale prototype will be tested in AAU