Study with us

Image credit: The University of Queensland

Our Centre’s objective is to enable Australian organisations to achieve responsible, secure, and agile value creation from data. We will build workforce capacity in Data Science, Machine Learning and Artificial Intelligence.

As a HDR student with CIRES, you will have the opportunity to work collaboratively with university and industry partner supervisors on research with real world impact. During your degree, you will undertake a 12 month industry placement with the industry partner.

 

We offer a generous scholarship package and will be recruiting for projects throughout 2022.

We currently have four PhD positions advertised below, with applications closing in October 2022.

 

PhD Scholarship: Developing Analytics-Driven Organisations

The ARC Training Centre for Information Resilience (CIRES) invites highly motivated and committed candidates to apply for a fully funded PhD position focused on how data analytics can drive organizational transformation. In line with CIRES’s industry engagement objectives, the position is defined and co-funded in close collaboration with Aginic, an emerging leader in the field of analytics that employs agile and user-centric approaches to build their competitive solutions. This scholarship is one of two CIRES projects with Aginic related to organisational and transformational aspects of data, algorithms, and AI.

The successful candidate will become part of Aginic’s multidisciplinary analytics teams, or squads, and will collaborate and participate in industry-led analytics projects, as part of the process of conducting engaged research. This project will develop a systematic and organized approach to data-driven transformations and will help Aginic and other companies progress data-driven transformation journeys.

For this position, CIRES is seeking a candidate with interdisciplinary interest and capabilities. The candidate will need knowledge in at least one of three areas: (i) business management or management information systems, (ii) computer science or data analytics, and (iii) economics. We do not expect candidates to have knowledge/expertise in all three areas, but combinations of expertise will be highly useful and desirable. Experience with field research and qualitative methods will also be valuable.

 

Applications are now open and close on the 6th October 2022.

PhD Scholarship: Data as a Service Architecture

This is an opportunity for a highly motivated student to join the CIRES project team, collaborating closely with leading experts in the Queensland Police Service (QPS) to develop a prototype system to showcase the scalability, reliability, and usability of an AI-based data discovery system. This scholarship is one of three CIRES projects with QPS related to the responsible use of sensitive data assets.

For this project, CIRES is seeking a candidate with interdisciplinary interests and capabilities. The candidate will have qualifications relevant to this project, e.g., Master of Data Science, Computer Science, IT, and/or Bachelor of Computer Science, IT, Mathematics.

The candidate will have a good background in data science, data analytics, or machine learning, preferably with expertise in predictive analytics, graph mining, and causal inference. Experience working with and mining structured and unstructured data from multiple sources is also desirable.

Proficiency in Python programming language and machine learning software packages such as Tensorflow and Pytorch is required.

 

Applications are now open and close on the 6th October 2022

PhD Scholarship: Expanding Data Sets to Allow Improved Critical Care for Children – Outpatient Risk Prediction

This is an opportunity for a highly motivated student to join the CIRES project team, collaborating closely with leading experts in Queensland Health on investigating machine learning techniques for designing risk predictive models in outpatient settings. This scholarship is one of three CIRES projects with Queensland Health related to paediatric sepsis management. The successful candidate will collaborate with Queensland Health to develop a probabilistic based risk prediction system that identifies the future clinical abnormalities of children at risk of infection and sepsis.

This project seeks to develop a strong and capable future leader who can undertake medical analysis in a data-sparse environment, with the proposed model and research tasks able to be adapted and applied to other medical predictive tasks.

For this position, CIRES is seeking a candidate with an understanding of concepts from applied statistics/probability, numerical linear algebra, machine learning, algorithms and complexity. This project also requires proficiency in Python programming language, and machine learning software packages such as Pytorch. CIRES particularly encourages applicants with a medical background and relevant knowledge in the research domain.

 

Applications are now open and close on the 6th October 2022

PhD Scholarship: Expanding Data Sets to Allow Improved Critical Care for Children – Inpatient Risk Prediction

This is an opportunity for a highly motivated student to join the CIRES project team, collaborating closely with leading experts in Queensland Health on investigating algorithm use and organizational implications in inpatient settings. This scholarship is one of three CIRES projects with Queensland Health related to paediatric sepsis management. The successful candidate will collaborate with Queensland Health to develop a platform-independent decision support framework using an interpretable machine learning approach to make effective risk predictions for paediatric patients at risk of sepsis.

This project seeks to develop a strong and capable future leader who can undertake medical analysis in a data-rich environment. The developed model will demonstrate the flexibility to be adapted and applied to other medical predictive tasks, e.g., sepsis prediction/monitor, linking genomics with risk prediction, etc.

For this position, CIRES is seeking a candidate with an understanding of concepts from applied statistics/probability, numerical linear algebra, machine learning, algorithms and complexity. This project also requires proficiency in Python programming language, and machine learning software packages such as Pytorch. CIRES particularly encourages applicants with a medical background and relevant knowledge in the research domain.

 

Applications are now open and close on the 6th October 2022

PhD Scholarship: Curating Systems of Engagement

This project tackles the critical issue of how information from an organisation’s public systems of engagement can be captured, developed  and leveraged as information assets of value and will contribute to methodologies for improving enterprise information and knowledge management. Working with partner Astral Consulting, the research will investigate how organisations can leverage human & AI/ML approaches to curate and incorporate the uncontrolled data accumulating in an organisations systems of engagement into its formal systems of record. A system of record is the legal, auditable system that tracks records while a system of engagement is where end-users carry out their tasks, such as collaborate on and complete business processes.

This research will investigate how AI/ML can meet the demand of utilising the information within systems of engagement, including the full range of communication and collaboration technologies in use but currently outside of existing organisational control.

PhD candidates should have a background in Information Systems, or Enterprise Information Management with a computing science or similar technology background preferred, but not essential.  A strong interest in technology and the business value of advanced data analytics, AI and machine learning is an advantage.  Previous experience in business analysis, enterprise architecture and/or working with metadata, taxonomies and Information Architecture is beneficial, but not essential.

 

Applications will open in late 2022.

To register your interest in this project or find out more, contact us via cires@uq.edu.au