PhD - Gent | More than two weeks ago
The utilization of artificial intelligence in the field of bioprocessing is still in its infancy. Nevertheless, this technology offers the potential to predict and improve cell culture behavior provided that there is available data. Traditionally, processes are measured in a manual and time-consuming way, typically only at the beginning, middle, and around their expected end, making it challenging to precisely monitor the activities within the bioreactor and understand the root causes of occasional failures. To address this issue, a probe is currently under development at imec, which employs various sensors to continuously monitor a bioreactor process.
In addition to process monitoring, it is crucial to understand why failures occur at times, such as contamination or a significantly lower number of viable cells than anticipated. This doctoral research focuses on the use of global and local explainable artificial intelligence to predict, model and visualize when and why a process may deviate from the desired outcome and how it can be effectively corrected.
The PhD student will work within the AI&Data department at imec that supports hardware-software co-design with data analysis and machine learning modelling. The PhD student will be active on biomanufacturing hardware project(s) and will be in contact with the internal teams contributing to this.
Our ideal candidate for this position has the following skills:
Required background: Master’s degree in bioscience or pharmaceutical engineering with knowledge of machine learning and explainable AI or electrical engineering, computer science, mathematics or physics with expertise in biology or bioprocesses.
Type of work: Modelling, algorithmic and system design, experimentation, literature study
Supervisor: Steven Latré
Co-supervisor: Catherine Middag
Daily advisor: Catherine Middag
The reference code for this position is 2024-095. Mention this reference code on your application form.