The research program consists of five tracks with a focus on the application of AI and machine learning to create digital twins across multiple physical scales in industrial bioprocesses.

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Digital twin and smart plant scheduling: Developing an AI-based framework/advisory tool for optimal batch scheduling to maximize plant output of an enzyme production line, and balance product performance and quality against operational costs.

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Digital twin for large-scale fermentation: Evaluate how AI methods can be applied to develop an accurate real-time simulation of fermentation performance indicators in response to dynamic process inputs for industrial scale fed-batch operated fermentation units of 50-500 m3.

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Digital twin of lab automation processes and self-learning platforms: Developing autonomous systems that evaluate and develop ‘self-learning’ experimental design algorithms, combine data-driven approaches with mechanistic modelling and other things.

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Machine learning for genome-phenotype engineering: Evaluating and implementing so-called ‘representation learning’ methods on multi-omics data from strain development projects.

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Machine learning for iterative metabolic engineering: To determine how AI/ML methods can support efficient exploration of a large solution space for (iterative) strain improvement.