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Field
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, through developing predictive models and new experimental methods and instrumentation, to design creative and cost effective CO2 trapping processes. The need is urgent, the task is challenging and a
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methods to predict the origin and dispersal patterns of genomic sequences, with applications ranging from biogeographical mapping to paleogenetic reconstructions. The candidate will work jointly with Dr
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and localization of a potential fault using the Matched Field Processing (MFP) method, based on the reconstruction of a response model of the inspected structure from the modal parameters predicted by
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applications. A reverse modeling strategy will be used to design an optimum test matrix. This framework will enable modeling and predicting ion-irradiated mechanical properties using reduced test data, thereby
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, predictive models of neurodegenerative disease with a focus on Alzheimer's Disease. Computational models will be developed that utilize data obtained from a wide range of experiments, from basic biochemical
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uses a range of approaches, including in vivo and ex vivo mouse models, primary human cell culture, and in vitro organoid cultures. Responsibilities Responsibilities include conceptualizing and
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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, and publication of major results from the experiment. They will also lead the development of predictive distribution models that incorporate data from the experiment. The project is funded by the USGS C
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promoters. You will train and evaluate predictive models in model/crop species with different levels of genome complexity. You will work very closely together with your dry-lab colleagues for data processing
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/or high-temperature heat pumps based on power cycles. Design thermal and/or thermochemical energy storage systems. Implementing and validating advanced thermodynamic models for performance prediction