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trends. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular properties of catalysts together with statistical methods to derive predictive models
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materials, to aid design of novel more energy-efficient processing routes. The development of these digital twins requires reliable and predictive models for microstructure formation during steel processing
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of these digital twins requires reliable and predictive models for microstructure formation during steel processing. These models should be based on accurate predictions of phase stability and defect kinetics
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to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and
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. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective
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regarding the demand that will have to be met soon. This requires building robust models to predict future demand, making decisions under time heterogeneous uncertainty and adapting to the state
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predictive models and novel measurement methods that improve coating performance for corrosion protection, heat reflection and radiation shielding. You will collaborate closely with our industrial partners
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for translation and testing model predictions; bioinformaticians, investigating evolutionary conservation of sequence, (co)expression and regulatory modules; and modelers, developing crop-specific integrated plant
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, different time scales of predictability. Famous examples include various states in the transition to turbulent fluid flow or metastable chemical configurations. However, such transient stochastic phenomena
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collaborators design targeted experiments to test model predictions. Come be part of the vibrant Computational Biology community at Utrecht University! Your qualities You are a curious and collaborative colleague