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Bayesian Neural Networks for Uncertainty Quantification in Extrusion Rates

Ai Biofabrication

Neural Networks for Bioprinter Nozzle Optimization

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Ai Biofabrication › Neural Networks for Bioprinter Nozzle Optimization

Bayesian Neural Networks for Uncertainty Quantification in Extrusion Rates

This study develops Bayesian deep learning models that quantify epistemic and aleatoric uncertainty in bioprinter nozzle extrusion rate predictions across varying material and environmental conditions. The research advances understanding of confidence bounds in bioprinting predictions and enables probabilistic decision-making for clinical-grade biofabrication 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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