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technologies based on a digital twin. The digital twin will combine information from the physical structure with models and monitoring data to assess its current structural state and predict its remaining
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Are you a MSc graduate with background in data science, computer science, biostatistics, bioinformatics or a related field? Do you have a solid foundation in machine learning? Are you passionate
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multidisciplinary research project and collaborate with industry partners? If so, then this PhD position might be perfect for you. Information Can we reduce stress, thermal discomfort and energy consumption
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Are you fascinated by how curiosity shapes learning in the classroom? Would you like to investigate how children seek information, explore and learn in real-world educational settings? If so, then
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PhD Position: Decision-Making for Heat Transition Planning under Data Sharing Constraints Can grid operators and other stakeholders make robust decisions when the data they need cannot always be
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seeks to connect with social movements, protest, and conflict. Please follow the links to learn more about the Cultural Anthropology Department and the Sovereignty and Social Contestation Research
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carbon properties influence PFAS removal in drinking water treatment. You will combine laboratory experiments, column studies, and data analysis to identify the factors governing PFAS breakthrough
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. To address this challenge, the project aims to develop structural health monitoring technologies based on a digital twin. The digital twin will combine information from the physical structure with models and
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develop methods to correlate information across time, frequency, space and impedance. The aim is to identify interference sources and propagation paths and understand how disturbances spread through complex
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. Furthermore, you will apply these tools to help us investigate the potential causes and timings of flare ups during patients’ daily lives. You will work primarily with video data, musculoskeletal models, and