Associate Professor / Professor in Computational Statistics
Queensland University of Technology
School of Mathematics and StatisticsClosing date: 10th June 2018
The School of Mathematical Sciences at QUT has an exciting position for an Associate Professor, (Level D) / Professor (Level E) in Computational Statistics who is able to provide strategic leadership in both research and learning within the Discipline of Statistics and Operations Research. More widely, the Associate Professor / Professor will strongly contribute to QUT’s research priority in Data Science through the development of new computational methods in statistics and data analysis.
The Associate Professor / Professor will help to lead, maintain and expand our research expertise in developing new methodologies, knowledge and insights from the large amounts of data collected by government, business and industry. They will have expert knowledge and practical experience with techniques for evaluating analytically intractable problems such as Markov chain Monte Carlo, sequential Monte Carlo, methods for approximate inference, optimisation methods, big data analytics, resampling methods, inverse problems and uncertainty quantification.
The Associate Professor / Professor will have a strong record of high quality publications (among leaders in the field) and in the generation of competitive and commercial research income. They will also have a strong record of mentoring early and mid-career academic staff and the supervision of higher degree research students and be committed to the development and delivery of high quality learning experiences for statistics, operations research, engineering, science and health science students who want to combine their studies in statistics with real-world applications.
Necessary to the success of the position is the fostering of partnerships with both internal and external groups in developing research programs. The Associate Professor / Professor will link with the ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS), led at QUT, by Professor Kerrie Mengersen as well as other data science practitioners within the university including academics in the Data Science Discipline in the School of Electrical Engineering and Computer Science and the ARC Centre of Excellence in Robotic Vision led by Professor Peter Corke, which is hosted at QUT.
This position reports to the Head of School of Mathematical Sciences for supervision, workload management and for Performance Planning and Review (PPR).
**Mention you saw it on the AustMS website**