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the student to guide the research, but preliminary ideas include: Exploring whether suggested discharge limits derived from single organism experiments are protective of microbial communities; Using Bayesian
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Methods Monitoring biodiversity at scale is vital for meeting global conservation targets, yet manual field surveys are too expensive and time consuming. Automated systems like camera traps now generate
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strategy, a probabilistic (e.g. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material
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. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models for long-term forecasting. Collaborate closely with
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uncertainty from climate projections into land-use forecasts. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models
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career scientist with background in organic geochemistry, statistics, and Bayesian modeling to pursue analyses of paleoclimate biomarker data. The ideal candidate should be proficient with both laboratory
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. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set
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exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 17 days ago
specifically, we use simulation-based inference (SBI) [1], a Bayesian approach that leverages deep generative models, such as conditional normalizing flows and score-diffusion models, to approximate