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We seek PhD students that will contribute to new generations of scalable, model-based tools for cyber-physical systems based on a mathematical sound foundation, that enables trade-offs between
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science(e.g., mathematics, physics, computer science, etc.), a strong background in data science, statistics, or machine learning, and an interest in biomedicine. Alternatively, a candidate with a
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proper decisions. The trade-off between safety and greediness are assessed using stochastic constrained optimization. At the outset, it is assumed that the mathematical model of the system is known
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