PhD position in fluid dynamics of bioprocesses

In this project you will study the hydrodynamics of real broths, combining experimental assessment, development of state-of-the-art machine-learning analysis tools, and application towards process simulations. The main objective of the project is to quantify the impact of fluid composition and operating conditions on the hydrodynamics of aerated fermentation processes, using a range of measurements techniques suitable for opaque fluids, such as X-ray tomography. You will develop novel machine-learning algorithms to analyze the data, and to unravel the impact of fluid composition on hydrodynamics. The acquired data will provide a rich database for bioprocess researchers to develop and verify process models, and to support process design. Depending on your preferences, part of the project can be dedicated to developing/benchmarking process simulations, or otherwise to a more in-depth analysis of hydrodynamic structures in the acquired data.

Key focus points:
• Establish an experimental framework for hydrodynamic characterization using a range of fluid compositions by integrating multiple experimental techniques.
• Development of machine learning methods for data processing and/or analyzing the impact of fluid composition on hydrodynamic behavior.
• Development and verification of CFD simulations on acquired data, or, analysis of hydrodynamic structures in real broths.
The results acquired in this project are highly relevant for running and future projects in the bioprocess engineering section, and the data may additionally be used in future projects on visualization and fundamental properties of multiphase hydrodynamics of real fluids. Besides developing your scientific skillset, this project offers several collaboration opportunities. You will closely co-operate with students in bioprocess development and simulation, as well as with PIs in the fields of machine learning and fundamental hydrodynamics. Additionally, interactions with potential industrial partners are [stimulated]. Next to research, you will participate in educational activities such as assisting in courses, and supervision of BSc/MSc graduation projects