In order to overcome the difficulties with classical designs related to the dependence on an assumed statistical model, the number of blocks, sample size, and multi-stratum structures, Bayesian approaches are proposed along with the supersaturated and D-optimal designs in the literature. This project aims to explore the current literature on Bayesian supersaturated D-optimal designs and develop new Bayesian A- or D-optimal designs that are more cost-effective and can be used with multi-stratum structures such as split-plot designs, control type I error around the desired level and improve the power.

Where: Mathematical Sciences Discipline, School of Science, RMIT University, Melbourne Australia

Duration and Value: Scholarship for 3 years ($33,000 AUD per year) 

Closing date: 30/11/2022

Application: https://www.rmit.edu.au/students/careers-opportunities/scholarships/research/bayesian-approaches-to-screening-designs-of-experiments

Candidates who have completed a Bayesian course and a DoE course in their previous studies are highly encouraged. 

Inquiries should be sent to Dr Haydar Demirhan (haydar.demirhan@rmit.edu.au) or A/Prof Stelios Georgiou (stelios.georgiou@rmit.edu.au).