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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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. 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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identify therapeutic targets. These efforts will generate large-scale, rich in vivo perturbation datasets, requiring scalable and reproducible pipelines for guide demultiplexing and assignment, cell-type
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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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distribution of mid-ocean ridge earthquakes, quantify the associated energy release, identify clusters and mechanisms of interests and perform relative relocations on targeted event swarms. The seismogenic
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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 | about 1 month 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
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silico model of normal development. Bayesian inference will calibrate model parameters and highlight control points, with predictive accuracy benchmarked against existing perturbation datasets. O3. Map
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, identified key markers and regulators of disease progression are evaluated functionally in various human cell and tissue model systems to assess their potential as treatment or vaccine targets. Here you can
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molecular data. 3. Identify when and why embryos fail using targeted computational perturbations. This inherently interdisciplinary project lies at the intersection of developmental biology, computational