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Field
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Bayesian inference framework for identifying complex aerospace systems combining with limited experimental data. It can be also used to quantify uncertainties from experimental testing, significantly
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analysis methods, including mixed-effects survival models and mixed-effects models applied to plant growth; experience within a Bayesian framework is highly desirable. 3. Strong skills in data analysis and
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 26 days ago
Objective. The goal is therefore to develop explainable mechanisms to 1) clearly inform end users about the different risks of inference, and 2) give the user control what risk is acceptable or not to them
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modelling approach, and dynamic Bayesian Networks would be advantageous. Willingness to conduct research in a multi-national project team. Funding requirements: You cannot have resided in The Netherlands in
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. - Compilation of a curated catalog of archaeal genomes from public data and newly obtained data within the team. - Orthogroup inference, multi-clade pangenome graphs to detect genes with restricted distributions
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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. Funding
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work at the intersection of palaeogenomics, bioinformatics, and evolutionary biology to overcome long-standing barriers in analysing degraded or low-quality DNA, enabling reliable genomic inference
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revisited in light of these new findings. The impact of uncertainties -such as those related to kinetics and radiation- on simulations of spherical flames (typically used experimentally to infer
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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