|Location||University of Technology Sydney, Faculty of Science|
|Eligibility||Open to international applicants|
Algorithms for real-time infectious disease data processing
Infectious disease outbreak surveillance has been transformed by rapid advance in genomic diagnostics. Samples from infected patients are now routinely whole-genome sequenced by public health labs worldwide, with hundreds of new genome sequences being deposited to public databases every day. In order to use this information for public health investigations it is necessary to analyse the data in real-time.
This project will focus on developing and applying new statistical algorithms for phylogenetic inference on data streams. These work builds upon Sequential Monte Carlo methods, a well known class of statistical inference algorithms for streaming data sets, with applications outside biology in robot control and quantitative finance. There is flexibility in the project to work either on theoretical aspects such as convergence proofs, technical aspects such as software implementations, or applied aspects, analysing actual outbreaks as they occur.
Additional supervision will be provided by Dr Mathieu Fourment (UTS).
Desirable skills and qualifications:
• Strong knowledge of a programming language such as Python, R, or C++
• Knowledge of probabilistic modelling
• An interest in or knowledge of approximate inference algorithms
• Open to domestic and international students (who are able pay internal student fees) for commencement on or after July 2018
This is an Ausgem-funded research project. The successful candidate will be awarded $26,682 per annum over 3.5 years. To apply, please send your CV and a ½ page expression of interest.
New Zealand graduates are considered as domestic students and are exempt from international student fees.
Applicants are required to have the equivalent of a BSc Honours or Masters by Research degree and must also apply for admission to UTS' PhD degree program.
See our full disclaimer