ARC Training Centre for Information Resilience (CIRES) PhD Scholarships

Summary

Enrolment status New students
Student type Domestic students, International students
Level of study Higher Degree by Research
Study area Arts, Humanities and Social Sciences, Business and Economics, Education, Engineering and Computing, Health and Behavioural Sciences, Medicine, Science and Mathematics
HDR funding type Living stipend scholarship, Top-up Scholarship, Tuition Scholarship
Scholarship value A base stipend rate of AUD$34,627 per annum (indexed annually). A top-up from $5,000 per annum is also available.
Scholarship duration 3.5 years with the possibility of 1 extension in line with UQ and RTP Scholarship Policy
Opening date 22 September 2021
Closing date 24 October 2021

Description

Seeking future leaders to unlock the value of data in emerging digital technologies

This is an opportunity to be part of an innovative HDR training program provided through the Australian Research Council (ARC) Industrial Transformation Training Centre for Information Resilience (CIRES).

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 PhD researcher 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 one-year placement with the industry partner. We offer a generous scholarship package and are now recruiting for the projects across our five research themes as listed below.

Defining and Measuring Analytics Value

Defining and Measuring Analytics Value

The ARC Training Centre for Information Resilience (CIRES) invites highly motivated and committed candidates to apply for a fully funded PhD position focused on value creation from data and analytics. 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 methodology to define and measure the value of data and analytics for organisations. The methodology can provide Aginic and other firms investing in analytics with an evidence-based approach for ongoing value creation and measurement from data.

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 quantitative methods will also be valuable.

Prof Marta Indulska (Principal Advisor), Dr Ida Asadi Someh, Dr Thomas Taimre, [UQ], Mr Brett Thebault, Mr Rob Mackay [Aginic]

Developing Analytics-Driven Organisations

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.

Dr Ida Asadi Someh (Principal Advisor), Prof Marta Indulska, Prof Shazia Sadiq [UQ], Mr Brett Thebault, Mr Rob Mackay [Aginic]

Bias Mitigation in Human in the Loop Decision Systems

Bias Mitigation in Human in the Loop Decision Systems

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 generate more transparent, fair, and trustworthy decision-support systems driven by data and controlled by humans. This scholarship is one of three CIRES projects with QPS related to the responsible use of sensitive data assets.

The successful candidate is expected to have a good background in data science, data analytics, or machine learning and will develop novel bias tracking, management, and reduction method over the entire Artificial Intelligence pipeline: from data collection and curation to model training and deployment with end users.

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

For this position, CIRES is seeking a candidate with an understanding of concepts from applied statistics/probability, machine learning, algorithms and complexity, and human-computer interaction. This project also requires proficiency in the Python programming language, and machine learning software packages such as Pytorch or Tensorflow.

A/Prof Gianluca Demartini [Principal Advisor], Dr Hassan Khosravi, Prof Shazia Sadiq, Prof Rhema Vaithianathan (UQ), Mr Nick Moss [Queensland Police Service]

Data as a Service Architecture

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.

A/Prof Hongzhi Yin (Principal Advisor), Dr Rocky Chen, Dr Wen Hua, Prof Shazia Sadiq [UQ], Mr Nick Moss [Queensland Police Service]

Interpretable AI-Theory and Practice

Interpretable AI-Theory and Practice

The ARC Training Centre for Information Resilience (CIRES) invites highly motivated and committed candidates to apply for a fully funded PhD position focused on researching interpretable machine learning algorithms to understand how black-box models behave and provide theoretical foundations for algorithmic safety. In line with CIRES’s industry engagement objectives, the position is defined and co-funded in close partnership with the highly successful Brisbane-based consultancy – Max Kelsen. In collaboration with Max Kelsen Partner Investigator Dr Maciej Trzaskowski, an expert in machine learning and quantum computing, the candidate will develop applications for health and genomics data analysis using the data repositories held by Max Kelsen,

Max Kelsen has active research, development, and consulting activities in the fields of AI and cancer genomics, and has prioritized AI safety as a key ingredient of any new product prior to deployment. This scholarship is one of two CIRES projects with Max Kelsen related to organisational and transformational aspects of data, algorithms, and AI.

The candidate is expected to have good understanding of concepts from applied statistics/probability, numerical linear algebra, and machine learning. Proficiency in Python programming language and machine learning software packages such as Pytorch is required.

Dr Fred Roosta-Khorasani (Principal Advisor), Dr Hassan Khosravi, Prof Shazia Sadiq, Dr Sen Wang [UQ], Dr Maciej Trzaskowski [Max Kelsen]

Using Data to Overcome Wellbeing Challenges Across the Life Spectrum

Using Data to Overcome Wellbeing Challenges Across the Life Spectrum

This is an opportunity for a highly motivated student to join the CIRES project team, collaborating closely with leading experts in Health & Wellbeing Queensland to apply predictive modelling to datasets for useful insights into communities to drive innovation and change in clinical settings.

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. Qualifications in other disciplines (e.g., health science) with demonstrable expertise in solving data-driven problems are also welcome. 

The candidate will have hands-on experience with data science, data analytics, or machine learning tasks, preferably with expertise in predictive analytics, knowledge graph mining, and causal inference. Meanwhile, candidates with a science background, like nutrition, diet, and public health but with a data-driven skillset, also align with the scope of this project.

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

A/Prof Hongzhi Yin (Principal Advisor), Prof Andrew Burton-Jones, Dr Rocky Chen, Prof Shazia Sadiq [UQ], A/Prof Robyn Littlewood, Dr Sara Mayfield [Health & Wellbeing Queensland]

Customer Data Stories

Customer Data Stories

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 to improve data curation through a crowd-sourced approach. In line with CIRES’s industry engagement objectives, the position is defined and co-funded in close collaboration with Allianz Partners, the world’s largest diversified insurance company. This scholarship is one of two CIRES projects with Allianz related to organisational and transformational aspects of data, algorithms, and AI.

The successful candidate is expected to have a good background in data science, data analytics, or machine learning and will develop novel data-driven marketing methods making research contributions over the entire Artificial Intelligence pipeline: from data collection and curation to model training and deployment with end users including evaluation.

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

For this position, CIRES is seeking a candidate with an understanding of concepts from applied statistics/probability, machine learning, algorithms and complexity, human-computer interaction, and marketing. This project also requires proficiency in the Python programming language, and machine learning software packages such as Pytorch or Tensorflow.

A/Prof Gianluca Demartini (Principal Advisor, Dr Wen Hua, Prof Shazia Sadiq [UQ], Mr Shane Downey [Allianz Partners]

Human-Centred Data Literacy Curriculum for complex Educational Organisations

Human-Centred Data Literacy Curriculum for complex Educational 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 curriculum development for data literacy. The successful candidate will work in close collaboration with their advisory team, leading experts in the Queensland Department of Education (DoE) and school leaders and teachers to develop curriculum for a data literacy framework, design and undertake a field study for adaptive delivery of the curriculum, and evaluate the effectiveness of the developed curriculum and methods of adaptive delivery.

For this position, CIRES is seeking a candidate with interdisciplinary interest and capabilities. The ideal candidate will have a passion and experience in teaching and developing learning content (as a tutor or a teacher), experience with field or lab research using quantitative and qualitative methods as well as strong written and communication skills.

Dr Hassan Khosravi (Principal Advisor), Prof Shazia Sadiq, Prof Mark Western [UQ], Dr Sandra Nissen, Mr Nigel Pearn [Queensland Department of Education]

Community Attitude to Law Enforcement Data

Community Attitude to Law Enforcement Data

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 qualitative and participatory research methods that can be used by data-driven organizations to understand and better communicate the impact of using human data to customers, users, and the public. 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 a background in Information Systems or Psychology, with a strong interest in technology business value and trust. Prior experience/knowledge in qualitative and design methodologies is preferred.

Prof Marta Indulska (Principal Advisor), Prof Shazia Sadiq, Prof Rhema Vaithianathan [UQ], Mr James Hinchliffe, Mr Nick Moss [Queensland Police Service]

Expanding Data Sets to Allow Improved Critical Care for Children - Outpatient Risk Prediction

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.

Dr Sen Wang (Principal Advisor), Prof Andrew Burton-Jones, Prof Shazia Sadiq [UQ], Prof Lizbeth Kenny, Dr Adam Irwin, Dr Paula Lister [Queensland Health]

Expanding Data Sets to Allow Improved Critical Care for Children - Inpatient Risk Prediction

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.

Dr Sen Wang (Principal Advisor), Prof Andrew Burton-Jones, Dr Wen Hua [UQ], Prof Lizbeth Kenny, Dr Kristen Gibbons, Dr Adam Irwin, [Queensland Health]

Improving Sepsis Management through Better Data and Rapid Learning

Improving Sepsis Management through Better Data and Rapid Learning

This scholarship is an opportunity for a highly motivated student to join the CIRES project team, collaborating with leading experts in Queensland Health to understand how clinical teams can best leverage new AI risk prediction algorithms.  The scholarship is one of three CIRES projects with Queensland Health related to paediatric sepsis management.  Whereas the other projects focus on the technicalities of the prediction tools, this project focuses on how clinicians can best use the tools and implementation issues that need to be managed. 

The successful candidate will work with Sepsis Breakthrough Collaborative, a new initiative at Queensland Health, aiming to utilize machine learning algorithms for early detection of sepsis in children and adults. This project will help and support the QH team to minimize the risks and maximize the value of algorithmic decision making. In addition to helping to understand and improve the rollout and use of new risk prediction tools for sepsis, the knowledge from this project will have implications for how clinicians use a range of new digital health tools, as the sepsis case is an instance of a general trend occurring across the clinical specialties. 

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) clinical work (medical, nursing, or allied health), (ii) computer science or data analytics, and (iii) business management or management information systems.  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.

Prof Marta Indulska (Principal Advisor), Prof Andrew Burton-Jones, Dr Ida Asadi Someh [UQ], Prof Lizbeth Kenny, Dr Kristen Gibbons, Dr Adam Irwin, Dr Paula Lister [Queensland Health]

Eligibility

To be eligible to apply, you must:

  • meet the entry requirements for Higher Degrees by Research at UQ
  • be nominated by the Principal Advisor and enrolling school or institute at UQ
  • submit a 2 page overview with your application addressing:
    • Why you are interested in this project
    • What challenges you foresee in the project
    • What special insights or capabilities you would bring to the project
    • How you see the project and PhD as part of your career trajectory
  • complete an interview process with the UQ supervisory team and industry partner

We are committed to equity and diversity. Female applicants and people of Aboriginal or Torres Strait Island descent are encouraged to apply. Indigenous applicants may be eligible for the Aboriginal and Torres Strait Islander Research Scholarship base living stipend rate.

This scholarship is open to Australian citizens, permanent residents and International students who are currently in Australia at the time of application and commencement.

Before you get started

If this scholarship has rules, download and read them.

How to apply

To apply for admission and scholarship, follow this link. There is no separate application for scholarship because you will have the opportunity to request scholarship consideration on the application for admission.

Before submitting an application you should:

When you apply, please ensure that under the scholarships and collaborative study section you:

  1. Select ‘My higher degree is not collaborative’
  2. Select 'I am applying for, or have been awarded a scholarship or sponsorship'.
  3. Select ‘Other’, then ‘Research Project Scholarship’ and type in ‘CIRES’ in the 'Name of scholarship' field.

See an example of what you have to do

Learn more about applying for a higher degree by research at UQ

Contact

CIRES Centre Manager Kate Aldridge

Terms and conditions

Read the policy on UQ Research Scholarships.

A domestic part-time student with carer’s responsibilities, a medical condition or a disability, which prevents them from studying full time may be eligible for scholarship consideration, on a case by case basis.