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Sequential Monte Carlo for Non-stationary Bioprocess Parameter Inference

Ai Bioprocess Optimization

Uncertainty Quantification in AI Bioprocess Models

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Ai Bioprocess Optimization › Uncertainty Quantification in AI Bioprocess Models

Sequential Monte Carlo for Non-stationary Bioprocess Parameter Inference

This research investigates sequential Monte Carlo and particle filtering methods to track time-varying bioprocess parameters under non-stationary conditions and quantify adaptive uncertainty estimates. The work advances Bayesian filtering approaches for capturing parameter drift in long-duration fermentation processes.

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📚 Academic: Thesis & PPT assistance included🧪 Tech: Master the protocols hands-on📝 Research > 3 months: Publication co-authorship in a Scopus-indexed journal

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