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towards a new conceptual model that explains the individual dynamics of fatigue. The project will start with an analysis of already-collected data. You will use several forms of time series analysis
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European consortium, we bring together industry partners, municipalities, academia, learned societies, and patient organisations to cocreate, pilot, evaluate, and iterate a series of city-level strategies
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
for rapid and resilient decarbonization. Your research will combine time-series analysis, supervised and unsupervised learning, and explainable AI methods to uncover the dynamic patterns preceding
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
resilient decarbonization. Your research will combine time-series analysis, supervised and unsupervised learning, and explainable AI methods to uncover the dynamic patterns preceding technological
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) with cross-modal modelling of time series and sensor data. The project will also explore signal-to-text generation for human-readable explanations of complex sensor outputs and mechanistic
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bottleneck) with cross-modal modelling of time series and sensor data. The project will also explore signal-to-text generation for human-readable explanations of complex sensor outputs and mechanistic
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industrial partners. Its aim is to develop and apply foundation models tailored to Dutch industry needs, focusing on underserved modalities (e.g. sensor data, time series, multimodal signals) and edge
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how appropriate integration of smart components in the structure enhances operational functionality, taking into account a complex series of boundary conditions (base functionality, reliability
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time on the education and supervision of BSc/MSc students. The selected candidate is expected to finish the 4 year project by writing a PhD thesis that contains a series of peer-reviewed journal articles