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state-of-the-art machine learning methods to analyse high-dimensional (time series) data. For the selected candidate, there will be possibilities to influence the project and develop new project ideas
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for the governance of airborne wind and by investigating spatial, procedural and distributional issues in the deployment of AWE systems. Research will be based on qualitative social science methods, e.g. conducting
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methods such as optimization, filtering algorithms, predictors, etc. Software and coding skills with, e.g., Python, MATLAB, R, C++, Julia, potentially HIL. Excellent command of English in speech and writing
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, the methods employed within the section include, system engineering, optimization methods, multi-disciplinary design optimization, uncertainty quantification, data science, machine learning and other methods
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imaging methods by combining experimental and computational approaches in physics. You will be working with laboratory X-ray microscopes at DTU and at synchrotron beamlines, in close collaboration with
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! Responsibilities and qualifications As part of the MULTIBIOMINE project, you will develop computational methods that use novel strategies to uncover hidden features in large microbiome datasets. These features