Fully Funded PhD Project on Digital Twin Approaches for Automated Monitoring, Controlling and Improvement of Bioprocesses

A student is sought for an industrially focused PhD project on Digital Twin Approaches for Automated Monitoring,Controlling and Improvement of Bioprocesses within the ARC Research Hub for Digital Bioprocess Development.

The student will design, implement, and evaluate a digital twin technology for monitoring, forecasting, and improving bioprocesses, such as mammalian cell growth and recombinant protein production.

Research will be conducted in the School of Computing and Information Systems at the University of Melbourne (https://cis.unimelb.edu.au/) with time also spent at industrial sponsor CSL Innovation in Parkville (https://www.csl.com/our-company).

The successful student will have an excellent track record in Process Mining, Information Systems Engineering, Data Science, Mathematical Modelling, Computer Science, or a related discipline and be able to effectively communicate with academic and industrial teams. 

They will be interested in evidence-based modelling and improvement of real-world processes, automated process discovery and conformance checking, and simulation of continuous and discrete processes to obtain accurate models of historical processes that generalize well to future process instances.

Knowledge of process mining and data science methods, and experience with industry projects is advantageous but not a requirement.

Other desirable skills include experience in bioprocesses, data analysis, mathematical modelling, Java, Python, or other coding languages. 

The successful candidate will be diligent and passionate about research.

Applications by email to abel.armas@unimelb.edu.au providing a motivation letter, CV and grades transcript.

Project Leader: Dr Abel Armas Cervantes

Collaborators: CSL Innovation

Keywords: Process Mining; Digital twin; Bioprocesses

Disciplines: Information Systems, Computer Science