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.

When a large number of factors are considered in an experiment, the identification of active factors that may have a substantial impact on the outcome is needed for screening purposes. Computer simulation experiments include many factors. For screening, different designs, such as orthogonal supersaturated, definitive screening, A-optimal, or D-optimal designs are proposed in the literature. A- or D-optimal designs are generated by optimization codes to minimize the average variance or covariance of the parameter estimates of a given model, respectively. Supersaturated design can also be used along with A- or D-optimality.

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.

This project requires intense coding to implement developed methods. The successful candidate needs to have strong programming skills. In this sense, strong knowledge of R and MATLAB is required. Also, a strong knowledge of Bayesian statistics and the design of experiments are required. In this sense, the selection committee will be looking for at least one Bayesian analysis/modelling and one design of experiments course in the previous studies of the successful candidate or one journal paper in each field is required. Candidates who are not satisfying these requirements are not encouraged to apply.

Scholarship for 3 years ($33,000 AUD per year) 

Applications are now open

31 January 2023

One scholarship available. 

Eligible applicants must fulfill all the following 3 requirements.

  1. Have a Master of Science or Engineering with a research component (thesis).
  2. Have a Bachelor (Hons) of Science or Engineering.
  3. Have an excellent knowledge of programming in R.

Applications should be submitted by email to A/Prof Stelios Georgiou within a single email titled “Scholarship Application in Bayesian Approaches to Screening Designs of Experiments”.

In this email the applicant needs to attach

  1. A cover letter describing how the applicant’s skills and research experience address the selection criteria.
  2. A current CV, academic transcripts, and a list of publications (if applicable).
  3. At least one reference letter.

Candidates will be notified of outcome to nominated email.

Selection criteria:

  1. Degrees in Science or Engineering or equivalent (honours and MSc).
  2. An interest in design of experiments and statistics.
  3. Knowledge in using the R software in designing experiments and Bayesian Statistics.
  4. Ability to work both independently and in a team.

Incomplete applications will not be considered.

Statistics, design of experiments and programming is needed in this project

Associate professor Stelios Georgiou (stelios.georgiou@rmit.edu.au)

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Acknowledgement of Country

RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business - Artwork 'Luwaytini' by Mark Cleaver, Palawa.