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Field
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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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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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single-cell profiling and predictive artificial intelligence models, you will engineer synthetic promoters controlling context-specific gene expression in Arabidopsis. You will develop high-throughput
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very low; Propose characterisation of the soil properties collected from different studied farms; Test how to Improve soil organic carbon content using organo-mineral resources under controlled condition
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thermal and/or thermochemical energy storage systems. Implementing and validating advanced thermodynamic models for performance prediction and optimization. Collaborating with experimentalists and industry
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; Assess the effects of organo-mineral management on soil biological parameters, including soil fauna; Predict the dynamic of OM in the future global climate change by using Soil Organic Models. Mentor
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predicts battery performance and properties from fabrication line measurements. About you Hold (or be near completion of) a PhD/DPhil in Control Engineering or a related subject, with the possibility
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conducting lifetime modeling, developing advanced condition monitoring techniques, and applying data-driven analytics for lifetime prediction. You will play a central role in integrating experimental insights
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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
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, Agronomy, modeling, biostatistics, or related field The applicant should have documented knowledges in Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming