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for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice
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function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice of data structures, static analyses and compiler optimizations, parallelism and concurrency
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techniques (e.g., techniques for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference techniques
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target population. Such “opportunistic” data pose significant challenges for making valid inferences about population-level environmental metrics such as soil properties, biomass stocks, or map accuracy
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spread over the study area and fail to represent a substantial part of the target population. Such “opportunistic” data pose significant challenges for making valid inferences about population-level
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of surface-atmosphere interactions and limitations in current observational methods . Traditional remote sensing techniques are generally indirect, inferring evaporation from thermal imagery and reflectance
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Experience with using inference/machine learning tools and basic programming is a plus As a university, we strive for equal opportunities for all, recognising that diversity takes many forms. We believe
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Experience with using inference/machine learning tools and basic programming is a plus As a university, we strive for equal opportunities for all, recognising that diversity takes many forms. We believe
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with process safety and security concepts, accident modelling approach, and dynamic Bayesian Networks would be advantageous. Willingness to conduct research in a multi-national project team. Fluent in
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the inside out. Your role: Develop experimental approaches for imaging-based characterization of soft matter. Apply and advance continuum mechanics and machine learning techniques to infer mechanical