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this information will not be considered. Position #1 - experimental In this PhD study, you will evaluate and help optimize a highly diverse intercropping system. You will assess which combination of service crops
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project, you will develop machine learning models that learn from high-throughput experimental datasets to uncover structure–property relationships and guide the selection of new experiments. The datasets
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we address the following steps/questions: Characterize the molecular structure and molecular distribution of suitable polymer pyrolysis waste streams to aid modelling of filler-matrix interfaces
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will contribute to building stronger theoretical foundations for structural optimization, to designing new algorithms for such integer programs, and to applying structured integer programs to various
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develop and optimize these methods, with as special aim to enrich for antigen-specific antibodies. A secondary key objective is to improve bioinformatic and AI tools to analyse the mass spectrometry data
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increasing attention in logistics optimization. In last-mile deliveries, in particular vehicle routing, solving stochastic problems efficiently has been of increasing importance due to the ever-growing size
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combines methods from ecology, industrial design, and engineering to optimize and upscale and biodegradable structures that temporarily mimic key emergent traits using industrial-scale additive manufacturing
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, and single-asperity scratching. Correlate composition, process, and performance to establish structure-property-function relationships. Collaborate with Canon Production Printing to translate insights
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design models that enable the repositioning of AM equipment based on Defence requirements in conflict zones and other industrial contexts; investigate optimal levels within the product structure
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discovering, analyzing, and optimizing business processes. It does so by extracting insights from event logs, structured records that capture sequences of activities, such as steps in a loan approval workflow