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Field
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energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and in the future. At TU Delft, our
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rate, and wearable sensor data. Develop and validate comfort prediction models targeting high prediction accuracy using a dynamic driving simulator and real-world operating conditions. Integrate thermal
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engineering behind the development of fusion energy, a potentially limitless energy source. PPPL is also using its expertise to advance research in the areas of microelectronics, quantum sensors and devices
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translate them into actionable insights for climate control and crop performance. You will work with chlorophyll fluorescence imagers, leaf-level validation sensors, and modelling tools to support
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sensor measurements to embankment responses will be derived and validated using field data. These relationships will provide a framework for translating routinely collected monitoring data into actionable
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study. The research will begin with the analysis of baseline and historical data collected from wayside sensors installed on selected instrumented railway bridges. Building on these insights, an on-board
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interfacing between sensors and advanced digital circuits. You have expertise in analog-to-digital conversion, preferably in the context of sensor readout (high accuracy). Preferably, you also have experience
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studies. You will work with a rich variety of data sources, including dietary and lifestyle information, multi-omics data, functional measurements, sensor data, and clinical data. By integrating
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- One Method Does Not Fit All: Co-design of Context-aware Adaptive Mobile Authentication for Older Adults”, “2024.14974.PEX”, “DOI: https://doi.org/10.54499/2024.14974.PEX ”, funded by the Fundação para a
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grids. The group is currently active in applications such as Direct Current distribution and transmission grids and microgrids, electrification of mobility (charging of electric vehicles), and renewable