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for in-the-wild settings. The technical challenge of the project is to build predictive models that capture both the substance of negotiations and the dynamic patterns of human interaction. The real-life
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infrastructure, including facility blueprints and model factories. Proficiency in the integration of automation technologies, including robotics, real-time data analytics, artificial intelligence/machine learning
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settings. The technical challenge of the project is to build predictive models that capture both the substance of negotiations and the dynamic patterns of human interaction. The real-life scenarios of Red
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-informed machine learning) and integrating uncertainty quantification into these workflows. You are familiar with environmental or soil science applications (e.g., carbon, nitrogen, biomass modelling). You
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, the PhD researcher will develop physiological-model-based artificial intelligence technologies to assess patients’ recovery process, detect or even predict the occurrence of clinical adverse events like
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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in combination with other machine learning techniques, to create predictive models. You will engage in an interactive feedback loop with domain experts to analyze discovered models and remove any