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Field
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Description Overview: We are seeking a Postdoctoral Research Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted visual representation and
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exploiting this information to support food safety risk assessment remains a major challenge. Key difficulties include the integration of heterogeneous data, the handling of uncertainty and complexity, and the
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to analysis of experimental data. Of particular interest will be new approaches for tackling multimodal data, quantifying uncertainty, providing rigorous theoretical guarantees, and modelling complex physics
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estimation and sensitivity/uncertainty evaluation. Core responsibilities include developing and applying process models to simulate wastewater treatment processes across a range of influent characteristics and
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configurations. The software can assimilate large datasets and perform adjoint computations, inverse modeling, and uncertainty quantification. It has enabled breakthrough scientific discoveries and supported
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regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty bounds and deriving
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. Specifically, your research will provide critical insight for NGGM performance assessment and predictions. You are encouraged to visit the ESA website: https://www.esa.int/ Field(s) of activity/research
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Gaussian process regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty
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on either uncertainty quantification or how uncertainty should be expressed to users. https://unit.aist.go.jp/deihrd07/keiyaku_koubo/2025-airc_0043.html [Work content and job description] ・Develop uncertainty
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. Description: Aerosols have a profound impact on climate, influencing the energy budget through their direct and indirect effects. These effects represent an important source of uncertainty in greenhouse gas