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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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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
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parasite and warming in different combinations and sequences), and their interaction. Plant performance will be assessed by measuring life history traits and fitness and using hyperspectral imaging of both
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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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scientific/technical context The creation of industrial ecosystems, including industrial parks, involves the exchange of resources and sharing of infrastructure among different production systems. In circular
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, cargo, harbors etc. Large and deep AI models can be built using these data sets and machine learning, which can be combined with real-time satellite-based AIS data and sensors such as radar and algorithms
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the European Regional Development Fund. Subject description The subject includes signal processing with emphasis on development and optimization of algorithms for processing single and multi-dimensional signals
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advantages. We will provide the necessary hardware and software for the real-time control of the machine, but the candidate will be responsible for developing and implementing the control algorithms. A working