Two MSc scholarships (17,000/annum + fees) are available to NZ residents at the University of Canterbury, Christchurch (New Zealand):
Thesis 1
Topic: Expert elicitation and incorporation of phenological modelling into grape bunch growth models.
Description: Grape bunch growth modelling is an important aspect of yield prediction, which enables grape growers to make management decisions in an informed and timely fashion. A double logistic growth curve model has been developed within a Bayesian framework based on available grape development data. In order to improve the model, the research candidate will investigate the ways in which additional information on the effects of environmental and spatio-temporal factors may be obtained and incorporated into the model. The candidate will specifically liaise with the phenological modelling team and study the way to incorporate their results into the model.
The project is part of an MBIE-sponsored 5-year GYA program, which is a dynamic multidisciplinary project in close collaboration with the NZ wine industry, and has received the Gold Rating in 2018.
Skills required:
– understanding of Bayesian statistical methods
– ability to design and implement MCMC algorithms
– programming proficiency (R preferred)
– ability to communicate with non-statisticians
The starting date: Late 2019 – Early 2020.
Please write to elena.moltchanova@canterbury.ac.nz or daniel.gerhard@canterbury.ac.nz for further information.
Thesis 2
Topic: Posterior Passing.
Description: Bayesian statistics is often advertised as a seamless and natural way to proceed from an old state of knowledge to a new one by updating the prior distribution with new data to obtain a posterior distribution, and passing that posterior distribution on as a new prior. However, the second step rarely happens in practice. The candidate will study the ways in which posterior passing can be applied in the context of Bayesian grape growth models.
The project is part of an MBIE-sponsored 5-year GYA program, which is a dynamic multidisciplinary project in close collaboration with the NZ wine industry, and has received the Gold Rating in 2018.
Skills required:
– understanding of Bayesian statistical methods
– strong mathematical background
– ability to design and implement MCMC algorithms
– programming proficiency (R preferred)
The starting date: Up to Dec 2020.
Please write to elena.moltchanova@canterbury.ac.nz or daniel.gerhard@canterbury.ac.nz for further information.