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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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schools see the weaknesses in their school environment and predict the effect of possible interventions to improve the environment. In this position, you will collaborate closely with another PhD student
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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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-LYNCH (‘Validated non-invasive liquid biopsy tests for cancer PREDIction in LYNCH Syndrome’) which brings together contributors from many countries and with different types of expertise. Therefore, you
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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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collected in these trials will feed into the development of the Human Dietary Digital Twin—a computational model for the prediction of individual metabolic responses to complex meals. This modeling work will
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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