Skip to Content

Data Futures Institute PhD Scholarships

Be a future thought leader - Shape the future of Artificial Intelligence and data science for social good

The Monash Data Futures Institute aims to develop outstanding critical thinkers with a deep understanding of social, scientific and policy issues paired with training in the use of advanced Artificial Intelligence (AI) and data science.

Graduate research students receiving the Monash Data Futures Institute PhD Scholarship learn from leading experts from across the Humanities, Science and Technology and Medicine, and work on research topics that relate to disciplines in two or more Monash faculties.

Students in this program become thought leaders at the intersection of data science, AI and the human experience. The skills acquired are transferable across education, corporate, and government sectors, enabling graduates to apply their training to a broad spectrum of career paths.

The Monash Data Futures Institute PhD Scholarship provides a highly competitive stipend of $37,000 per annum for up to 3.5 years and a further $3,000 per annum in travel support (in addition to up to $16,700 one-off Health Insurance and relocation expenses).

Monash University is one of Australia’s Group of Eight - the most research-intensive universities in the country - and positioned in the top 100 universities globally. We have an international reputation for interdisciplinary research with an emphasis on global issues.


  • Tuesday 6 October, Expression of Interest Open
  • Sunday 18 October, Expression of Interest Close
  • 19-24 October, Candidates shortlisting
  • Monday 26 October, Successful candidates receive a letter of "Invitation to Apply"
  • Saturday 31 October, Full Application due via Monash University Portal

How to apply

Step 1. Outstanding students are invited to submit an Expression of Interest. Applicants need to provide evidence of qualifications equivalent to a First Class Honours 1 (H1) and relevant research training experience, in addition to the minimum qualifications for standard entry admission to PhD at Monash University.

Step 2. Select a project from the list below. Please read carefully the project description and experience or skills required and then select a project.

Project 1. Designing AI for aged care: how to increase trust and engagement in new technology
Project 2.  Integrating novel technologies and modelling tools to predict species’ responses to global change
Project 3. De novo generation of superior polymyxins using AI and big data
Project 4. AI and Transitions in Evidence Use for Policymaking: A Cross-National Study

Step 3. Complete the Expression of Interest (EOI) form by Sunday 18 October (Please note: as part of the EOI, you’ll be asked to upload a current CV, a transcript and any other supporting documents such as published papers, and to indicate your selected project)

Step 4. Invitation to Apply. Successful applicants receive an invitation to apply for admission.

Step 5. Apply to Monash University for admission to the PhD program. We encourage you to refer to the Monash Graduate Research website for details on how to prepare your documentation and to submit your application. The next round of applications closes on Saturday 31 October.

Step 6. Wait for your offer. Applications for admission can take up to six weeks to be processed. Scholarship outcomes are released approximately six weeks after the scholarship round has closed. After scholarship outcomes are released, applications for admission will be assessed.

Useful links

Monash Data Futures Institute PhD Scholarship Projects

Project 1. Designing AI for aged care: how to increase trust and engagement in new technology

Main Supervisor

Professor Alan Petersen, Faculty of Arts


Associate Professor Adrian Carter, Faculty of Medicine, Nursing and Health Sciences
Dr Mor Vered, Faculty of IT
Dr Barbara Barbosa Neves, Faculty of Arts

Project Description

Are you interested in undertaking research that advances the wellbeing of older people? Do you wish to explore the role that technology—specifically AI—may play in empowerment in later life?

This exciting project focuses on designing AI for aged care to increase trust and engagement in the technology.

AI, if used responsibly, offers great potential in improving the quality of aged care. Yet, this potential will only be fulfilled if people trust the technology and those providing it. Since the majority of current AI research is data driven this includes user confidence in the way data is collected, secured and analysed, and  confidence that it will benefit older Australians and the community as a whole, it is crucial that key stakeholders, particularly researchers, developers and end- users, collaborate in a  co-design, user-centric process.

This project will engage with stakeholders and end-users to examine their views and attitudes towards current and future AI technology as applied to aged care, aiming to fill in the gap between the promise of existing technologies and what end-users need. It will shed light on how AI may be designed and implemented to ensure that it is trusted by aged care residents and their families, health and aged-care professionals, and the public to address needs and aspirations of older people. Your role will be to design and undertake fundamental research on this topic, engage with relevant stakeholder communities, and contribute to communicating the findings to both scholars and the wider community. Through undertaking your PhD, you will have the opportunity to contribute to research of critical importance to the community; develop your professional networks, including with members of the Monash Data Futures Institute and our research partners at the University of Oxford and the University of Copenhagen; gain training in the use of different research methods; and be well positioned to build your career in a rapidly expanding field. This is an interdisciplinary project, supervised by a team comprising researchers from Sociology, IT and Medicine at Monash University. While the scholarship is not restricted to particular fields, we believe it is likely to be especially appealing to those with an interest in science and technology studies, sociology of health and medicine, psychology, sociology of ageing, and/or human-agent interactions.

Areas of research

AI, data science, ageing, aged care, co-design

Experience or skills required

Quantitative and qualitative social research skills, in particular analyses of surveys, interviews, and documents; demonstrated interest or expertise in studies of aged care; strong written and verbal communication skills; ability to work independently under the direction of the supervisors.

Apply now

Project 2. Integrating novel technologies and modelling tools to predict species’ responses to global change

Main Supervisor

Dr Reid Tingley, Faculty of Science


Associate Professor Alan Dorin, Faculty of IT
Professor Carla Sgro, Faculty of Science
Professor Bernd Meyer, Faculty of IT

Project Description

Species’ distributions are shifting in response to global climate change and other human pressures. Accurate methods to monitor and predict distribution shifts are urgently needed to manage threatened species and ecosystems, and to control invasive species and diseases. This requires a step-change in the data and methods used to monitor and predict organism behaviours and ultimately shifts in species' distributions.

This project harnesses research in ecological and agent-based modelling, machine learning, and AI to increase the predictive power of models of species’ distribution shifts via “data-model fusion”. The successful candidate will develop near-real-time integration between emerging environmental and ecological AI-driven data sources (e.g., automated acoustic and machine vision species classification, web scraping, environmental DNA, satellite imagery) and agent-based models and ecological simulations. Our recent work, funded by the Monash Data Futures Institute, has piloted the development of novel data sources and agent-based models for the Australian cane toad invasion. The successful candidate will use this case study as a foundation for integrating multi-paradigm modelling approaches and devising workflows that are generalisable to a broad range of taxa and questions.

Areas of research

AI, Machine learning, Ecological modelling, Agent-based models

Experience or skills required

This position would suit a candidate with a computer programming background who has an interest in ecology and biodiversity conservation, or an ecologist with computational modelling experience (e.g., using R, Python, Matlab).

Apply now

Project 3. De novo generation of superior polymyxins using AI and big data

Main Supervisor

Associate Professor Jiangning Song, Faculty of Medicine, Nursing and Health Sciences


Dr Daniel Schmidt, Faculty of IT
Professor Jian Li, Faculty of Medicine, Nursing and Health Sciences

Project Description

Background and Aim

The World Health Organization (WHO) has identified antibiotic resistance as an urgent serious global threat. Acinetobacter baumannii, Klebsiella pneumoniae and Pseudomonas aeruginosa are the three WHO top-priority ‘superbugs’ against which polymyxins are the last-resort and no new antibiotics will be available in the near future. Unfortunately, kidney toxicity has limited the optimal use of polymyxins and resistance has emerged. To date, all polymyxin discovery programs have relied on empirical strategies and lacked consideration of the interactions of polymyxins with bacterial cell membranes. We have recently developed structure-activity relationship (SAR) models for polymyxins, and synthesised ~2,000 analogues for mechanistic investigations. Underpinned by our internationally leading research in antibiotic pharmacology, data-driven bioinformatics, and drug development, this project will be the first to employ cutting-edge all-atom molecular dynamics, generative adversarial networks, Big Data, deep learning, and AI to design and generate de novo superior polymyxins against bacterial 'superbugs', and target the significant global challenge due to antibiotic resistance.


This project will address the significant challenge in designing and discovering new-generation polymyxins against MDR A. baumannii, K. pneumoniae and P. aeruginosa, which are the top three high-priority Gram-negative ‘superbugs’. “No action today, no cure tomorrow.” As antibiotic resistance has seriously threatened modern medicine, the WHO has urged all government sectors and society to act on it urgently. This project will develop a cutting-edge data-driven systems approach by combining big data, machine learning, molecular dynamics and AI to design new-generation polymyxins. Notably, we are the first in the world to use this multi-disciplinary approach to discover new-generation polymyxin drugs. Importantly, our project will paradigm shift the current antibiotic discovery.

Areas of research

AI; Big Data; Antimicrobial Resistance; Superbugs; Next-generation Polymyxins; Smart Drug Design

Experience or skills required

Big Data; Bioinformatics; Computational Biology; Data Mining; Deep Learning; Machine Learning; Software Engineering.

Apply now

Project 4. AI and Transitions in Evidence Use for Policymaking: A Cross-National Study

Main Supervisor

Professor Michael Mintrom, Faculty of Arts


Professor Patrick Olivier, Faculty of IT
Professor Rob Raven, Monash Sustainable Development Institute
Dr Mitzi Bolton, Monash Sustainable Development Institute

Project Description

Are you passionate about novel solutions to longstanding public problems? Keen to identify approaches to sustainability transitions that can be taken up by public decision-makers within their existing day-day work? Looking to have your experience in public policy processes and interests in data science recognised and expanded? Then this is the PhD project for you!

Governments everywhere face significant challenges in devising public policies that are effectively informed by relevant evidence. Even with optimal public decision-making processes in place, heuristics, complexity, and resource limitations can lead to less than ideal outcomes.  AI holds ever-expanding potential to transform how data assembly and analysis generate insights into complex social, economic, and environmental processes. Hence, AI could contribute to transitions in socio-technical practices and public outcomes through major shifts in the institutions, techniques, practices, and use of evidence by policymakers.

However, successful uptake and use of such sophisticated data techniques and insights within incumbent policy regimes can be challenging, stalling the transformative capabilities of AI as a decision-making tool. The technology itself also presents challenges and risks, including the biases of those who build it manifesting in its operation, and concerns about ethics and overreach.
This PhD will combine policy science insights on public policy transformation with transitions insight on socio-technical systems transformation to offer a new and more integrative way of understanding the potential and challenges of mainstreaming AI into complex public problems settings. Ultimately seeking to better understand and explore how systematic approaches to data collection and analysis, as encapsulated in AI, can improve public outcomes. You’ll be joining and guided by an interdisciplinary team of experienced and recognised experts.

The doctoral candidate will:

  • Review selected AI approaches to understand expectations and claims made about their suitability and benefit for solving complex public problems.
  • Identify and compare the approaches of selected governments who have been fundamentally changing (transitioning) their processes and practices, with the support of AI, to improve the quality of evidence upon which public decisions are based.
  • Explore and analyse the perceived influences impacting the appetite for and success of such transitions, and reflect on the impact such evidence has in improving public outcomes.
  • Provide recommendations and/or co-design with AI developers and decision-makers on novel pathways for mainstreaming the use of AI to enhance complex decision-making processes and outcomes.

The final thesis will present a conceptual discussion of how AI could better inform policymaking, and an empirical discussion of several cross-national case studies concerning a single policy domain (preferably one that falls within the scope of the UN Sustainable Development Goals), resulting in a robust understanding of barriers and opportunities for the use of AI within public decisions, and possible tools for accelerating the adoption of AI for good in policymaking practices.

Areas of research

Applications of AI;  Technology in governance; Public policy development; Public decision-making; Socio-technical transitions; Ethics; SDGs.

Experience or skills required

Knowledge of public policy processes; Interest in data science and its application to decision-making; Preferably with relevant work or research experience.

Apply now