Applications for this PhD have now closed.
Location Queensland University of Technology, Science and Engineering Faculty
App. deadline 01/09/2017
  • Scholarship available
Eligibility Open to international applicants

Five PhD (Information Technology) Positions at the Business Process Management (BPM) Discipline, Queensland University of Technology (QUT), Brisbane, Australia

We are looking for five prospective PhD candidates to work on various topic of Business Process Management (Process Data Analysis - Process Mining, Process Querying, Machine Learning-based Process Models, Process Model Repair, and Responsible Process Mining) within the Business Process Management Discipline ( at the Queensland University of Technology (QUT), Brisbane, Australia.

Full three-year scholarships (including fees and living allowance) are available to suitable PhD candidates. Interested candidates with strong Information Technology, Computer Science or Mathematics backgrounds are encouraged to apply. Please get in touch with us by sending your CV (plus evidence of English Proficiency where applicable) to Associate Professor Moe Thandar Wynn (  Further information about the BPM discipline at QUT and our research strengths is available below. Please indicate the research area that you would like to pursue in your application.


About the BPM Discipline at QUT
QUT’s BPM discipline is regarded as one of the leading BPM research groups in the world and is known for conducting rigorous and relevant research applicable to and tested in the real world.  Our research is grounded in real-world problems and requirements as a direct result of strong collaborations with a well-established network of Australian organisations. Members of our discipline have authored some of the world's leading BPM textbooks, and have been invited as keynote speakers to some of the most prestigious BPM conferences in the world.  We cover both technical and business aspects of BPM using conceptual-analytical and empirical research. We're known for our involvement in workflow patterns research and the open source workflow environment YAWL.

Research Strengths
Process data analysis
In the area of big process data, we are working on process mining in areas like insurance claims processing and patient flow analysis, where event logs are analysed for evidence-based process improvement. This requires sophisticated real-time analysis techniques for predictive monitoring combined with interactive, insightful visualisations. We have engaged with Australian organisations on process-mining projects in the insurance, health, retail, transport and mining sectors.

Process modelling
We’re working on problems related to managing large collections of process models, which organisations tend to create thousands of over time. For example, process model merging and versioning, searching within process models, and refactoring of a process model collection. We continue to pursue a blended research approach merging technical and empirical strategies.

Process querying
We’re working on process querying. Process querying studies automated methods related to filtering or manipulating repositories of models that describe observed and/or envisioned processes, and relationships between the processes. A process querying method is a technique that, given a process repository and a process query, systematically implements the query in the repository, where a process query is a (formal) instruction to manage a process repository.


Process-based transformation
Beyond the management of transactional process performance, our researchers are working on how to improve transformational process performance based on the design and successful utilisation of process-based innovation systems. In close collaboration with corporations from the logistics, finance and professional services sectors, we are working on methods including opportunity-driven process improvement, positive process deviants and rapid process redesign methodologies.

Responsible Process Mining
Conducting process mining analyses could inadvertently produce outcomes that may have undesirable consequences, for example, violation of employees’ privacy. In line with the Responsible Data Science movement (, our researchers are working on how process mining can leverage information security principals (such as confidentiality, authenticity, integrity, non-repudiation) and technologies (such as cryptography) to deliver end-to-end responsible process mining practices.


Machine Learning-based Process Models


In collaboration with CRC ORE (, our researchers are working on how process mining can be synthesised with machine learning techniques to analyse streaming process data, such as sensors readings from devices in an ore processing plant, to profile and predict their performances. We seek to produce methodology and tools to assist users in creating high-fidelity machine learning models for various equipment in a plant/factory using real-time sensor data.

Next steps
Please send your CV to Associate Professor Moe Thandar Wynn ( and indicate your preferred research topic(s). You may then be asked to undertake an entry test and an interview.  We will then support you to apply for entry to PhD studies and the scholarship through QUT Annual Round (closing date: Sep 2017).

Please Note: Should this listing mention details of an available scholarship, it is your responsibility to confirm the specifics with the university / institute prior to applyiing. Terms and conditions are in some cases subject to change and are not always reflected immediately within listings.

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Further Information / Application Enquiries

A/Prof Moe Wynn