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Field
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and Control Section is to perform research and train next-generation students [BB1] on the topic of understanding and predicting the dynamics of complex engineering systems in order to develop advanced
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of Mechanical Engineering] This project aims to devise innovative monitoring technology for high-tech systems to perform (semi) autonomous fault isolation, and predictive monitoring. For high-tech equipment
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prescribing, it becomes also increasingly important to consider how non-genetic factors, like concomitant medication use and co-morbidities, together impact genetically predicted drug-metabolizing phenotypes
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an advantage. To address the latter challenge, the PhD candidate will build on Halbertsma et al. (2020) and Elshout et al. (2021) to develop and validate functional MRI-based biomarkers capable of predicting
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new survey data about attendance, and analyzes longitudinal data on participation. Examples of questions that will be answered are: How can we disentangle age and cohort effects in order to predict
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. Process simulation software is being developed for virtual optimization of tool design and material handling, enabling first-time-right manufacturing. The predictive quality of these tools relies
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these models in real tissue context, revealing how spatially organized cell communities rewire under genetic perturbation. Key challenges Use and improve single-cell foundation models to predict SNP-driven
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sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models that can accurately predict component health and
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to monitor patients’ health condition continuously and accurately after surgery to measure and evaluate patients’ recovery progress, timely detect and even predict clinical adverse events like delirium
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these models in real tissue context, revealing how spatially organized cell communities rewire under genetic perturbation. Key challenges Use and improve single-cell foundation models to predict SNP-driven