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, computer science, bioinformatics, computational biology or biostatistics Proven expertise in machine learning applied to molecular or other high-dimensional data Experience working with IBD or other immune-mediated
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, interdisciplinary working environment In-house modelling, data processing and data assimilation expertise, software, and High Performance Computation (HPC) infrastructure Excellent scientific infrastructure
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the electrical grid, electrolysers, fuel-synthesis processes, energy storage and heat integration in integrated Power-to-X systems. You will develop and apply physics-based process models that describe
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); contributes to SDC’s master programmes through teaching and guest lectures (Digital Innovation, research methods, computational methods, and case material from the candidate’s own fieldwork); contributes
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the development of high-performance indoor environments and energy performance in our future buildings, and advancing the transition towards more sustainable construction sites. We are seeking a candidate with
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At the Faculty of Engineering and Science, Department of Materials and Production a PhD stipend is available within the doctoral programme Materials Science, Mechanical and Manufacturing Engineering
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and other postdoctoral researchers as part of our Lundbeck Professorship grant, which you can learn more about here: https://www.cnap.hst.aau.dk/lundbeck-professorship As a postdoctoral researcher your
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patterns representing health incidents and the development of privacy-preserving methods for visualizing health data. What you will gain: Strong expertise in statistical and computational methods for privacy
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competences and extensive state-of-the-art equipment. You will have access to dedicated facilities for developing hyperspectral imaging instrumentation, advanced analytical laboratories, and high-performance
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will gain: Strong expertise in statistical and computational methods for privacy Experience working with unique, real-world health data Collaboration with an interdisciplinary research team across data