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microbiome and genome data to learn contextual representations of microbes and communities and translate them into predictive models for successful crop microbiome engineering. Your job Plant-associated
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Postdoc Position in AI Foundation Models for Crop Microbiomes Faculty: Faculty of Science Department: Department of Information and Computing Sciences Hours per week: 36 to 40 Application
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the pragmatism that is needed to move a project forward. By the time the position starts, you have obtained a PhD degree in Data Sciences, Computer Sciences, Physics, Earth Sciences, Civil Engineering, or a
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into solid phases of marine snow. You will test the performance of your model by simulating experimental and field data in collaboration with the PHOSFLUX PhD student. Your qualities We are looking for a
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develop the urgently needed system understanding, data analysis tools, and forecasting models for science and expert users, and co-create common ground among stakeholders for future-proof coastal management
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partners across the STURDI-Water consortium and translate model results into information usable in the project’s broader decision-support work. Publish the scientific results in a peer-reviewed journal
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(e.g., walking, cycling) related physical activity; finetuning, evaluating, and improving model performance using local data and ensuring explainability, fairness, and robustness of the model outputs
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. Together, we want to explore how small models can extract useful information on specific crime themes, such as drug trafficking, primarily from publicly available news sources. Relevant tasks include
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the water, as well as a broad variety of shops and boutiques. More information For informal information (not for application) about this position, please contact the project leader Dr Cale Miller
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Survey (ESWS) data from 9 European countries and a multilevel design with employees nested in teams nested in organisations. Advanced statistical techniques will be used to perform the analyses. You will