PhD in Numerical Methods

Fermentation-based manufacturing occupies an ever-increasing share the global economy, from food production to the pharmaceutical and chemical industries, and is a vital part of the green transition. Due to the complexity and heterogeneity of the biological mechanisms underlying fermentation, achieving optimal performance often requires fine-grained, time-dependent, control of the local environment. Ideally it would be possible to control the process optimally, without many expensive and time-consuming experiments or the need for complex online feedback mechanisms. In practice, process optimisation is costly and unpredictable, impeding the sustainability and economic viability of fermentation-based manufacturing. To fix this, you will develop and test new methods for data-efficient, open-loop optimisation of fermentation control profiles, building on recent theoretical developments in optimal control theory, reinforcement learning and numerical methods as well as laboratory analytics and automation. Ultimately, the goal is to automate fermentation process optimisation, thus enhancing existing fermentation based manufacturing techniques and making new ones possible. Location: Copenhagen, Denmark.