PhD Project in Bayesian Approaches to Screening Designs of Experiments
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).