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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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analysing time-series data from smartphone sensors and usage logs using advanced statistical and machine learning approaches to develop personalised nudging and adaptive interventions. The candidate will also
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through a mixture of qualitative, quantitative, and sensor based approaches to assess student experiences. Rather than seeking to “fix” individual students, our project challenges stigma by addressing
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climate control to direct crop-centric control. This paradigm shift relies on breakthroughs in microclimate sensing, interpreting crop performance by integrating sensor data at different temporal and
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future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which
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: Semantically Safe Robot Interaction (ID: TUEILSY-PHD20240930-SSR) Distributed Multiagent Planning and Control for Large Aerial Swarms in Changing Environments (ID: TUEILSY-PHD20240930-SAS) Safe Collaborative
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17.05.2026, Academic staff In networks of robots and unmanned aerial vehicles, the ability to exactly coordinate the observations of the different devices is key for successfully carrying out
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16.05.2026, Academic staff For applications in networks of robots and unmanned aerial vehicles, reliable coordination is essential. Such systems are particularly vulnerable to jamming and
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sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits
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conditions, geometries, parameters, sensors, or forcing terms; and exploring their potential to accelerate or complement conventional numerical simulations. The project is methodological in nature and is not