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control study collecting wearable-based data and hormone profiles from healthy subjects (20) and PAI patients (20) at four different doses of hydrocortisone Integrate wearable-derived physiological data
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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. It will use signals from different sources—such as radio signals and internal sensors— to maintain robust and accurate PNT, even when satellite signals are weak or missing. A built-in intelligent
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models for the integration of healthcare data specifically incorporating patient preferences. This includes challenges such as how to adapt to different perspectives and data models as well as ensuring
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issue is the embedding of hierarchies, for which a different geometry is better suited, namely hyperbolic geometry. Seminal works have shown that for embedding hierarchies, we should abandon Euclidean
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such as how to adapt to different perspectives and data models as well as ensuring high quality and robust integration. The PhD is situated in the Intelligent Data Engineering Lab and will be supervised by
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collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL (Prof. Olga Fink). IMOS focuses on the development of intelligent algorithms designed to improve the performance
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silver artefacts. Specifically, we will seek to understand what detail is being missed, using current assaying approaches. The project will showcase what insights, at different length scales, could be seen
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selectivity is the first important barrier to overcome in order to perform quantitative analyses for each pollutant and avoid ionic interference between the different sensors used in the project. Sensor
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garden experiments to test how plant performance is determined by plant genotype, the environment (above- and belowground parasite and warming in different combinations and sequences), and their