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that help determine when an AI model is ready for use and when more research is needed. PhD in Epidemiology on value-of-information from validating clinical prediction models and AI Our goal: Develop value
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models and Bayesian approaches to tackle complex, real-world data? Join this PhD project to build dynamic models and study cognitive variability using ecological momentary assessment (EMA). Join us We are
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. The lack of knowledge is related to the models that should be used to auralize UAM in urban environments: new models are needed to predict noise exposure in urban cities. Traditional aircraft noise studies
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failure (if they can), but they cannot explain why it happened or calculate the exact contribution of each individual stressor. Furthermore, these models often fail and make overconfident predictions when
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: formulate and analyze stochastic models of evolving populations using methods from statistical physics, applied probability, and population genetics; develop inference frameworks that link model predictions
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statistical physics, applied probability, and population genetics; develop inference frameworks that link model predictions to genomic and epidemiological data; design controlled computational experiments
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state-of-the-art tools and AI libraries developed at the TU/e, such as GameBus and Experiencer. The collected data forms the basis for developing predictive AI models that tailor coaching content and
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on building dynamic system models for both the energy conversion technologies and the greenhouse climate, integrating these into a unified framework suitable for state estimation, predictive control, and
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captured from UAVs. The research will address the design of AI models capable of combining heterogeneous sensor modalities, including RGB, thermal, LiDAR, acoustic arrays, GPR, and X-ray backscatter
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underexplored. Rather than functioning as neutral tools, algorithmic models translate complex realities into scores, rankings, classifications, and predictions. In doing so, they shape how problems are defined