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Statistics PhD Projects in Australia

We have 7 Statistics PhD Projects in Australia

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Showing 1 to 7 of 7
  Analysis of clinical trial and electronic health record data in advanced breast cancer
  Dr A Hopkins, Prof MJ Sorich
Application Deadline: 15 January 2019
Opening date. 26/11/2018. Closing date. 15/1/2019. Project Title. Analysis of clinical trial and electronic health record data in advanced breast cancer.
  Analytic methods for detecting and making sense of somatic genomic rearrangements
  Prof A Papenfuss
Applications accepted all year round
Bioinformatics is the application of mathematics, statistics and computational methods to the analysis of molecular biology data.
  Clinical cancer bioinformatics: developing new bioinformatics methods for improved analysis of cancer gene hybridisation capture panels
  Prof A Papenfuss
Applications accepted all year round
Bioinformatics is the application of mathematics, statistics and computational methods to the analysis of molecular biology data.
  Immunostaging for melanoma to accurately predict the risk of relapse after surgery
  Prof A Papenfuss
Applications accepted all year round
This project will utilise a large biobank of clinically annotated samples from patients with melanoma at high risk of relapse after surgery to develop a signature that predicts risk of relapse.
  Adaptive approaches for predictive modelling of Complex Multilayered Networks
  Dr K Musial-Babrys
Applications accepted all year round
Nowadays, we have the possibility to process ‘big data’ (gathered in computer systems) about the interactions and activities of millions of individuals.
  Automatic composition, optimisation and adaptation of multi-component predictive systems
  Prof B Gabrys
Applications accepted all year round
Applications are invited for a 3 year, fully funded PhD research studentship to work on a project entitled "Automatic composition, optimisation and adaptation of multi-component predictive systems".
  Development of robust and scalable hyperbox based machine learning algorithms
  Prof B Gabrys
Applications accepted all year round
The research conducted in this project will explore (fuzzy) interval, hyperbox or hyper-rectangle representation of the data as a basis for developing robust, scalable machine learning algorithms and evaluate their potential for reconfigurable hardware (for instance in FPGAs) and in-database implementations.
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