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$6.2 million has been awarded to the Next Generation Emerging Technologies Graduates program and $4.2 million for the Next Generation Artificial Intelligence Graduates program. The Next Generation Graduates programs will co-fund student scholarships with each program's industry and university partners. These programs will allow students to be part of a multi-disciplinary team aimed at solving a real-world challenges.

The programs will provide scholarships to domestic students (Australian Citizens and Permanent Residents) only.

Enrolments will be managed by program Chief Investigators and their university, we anticipate they will open in May 2022. To receive up to date information about how to apply to the program as details become available sign up now.

Below are the round one programs for the Next Generation Artificial Intelligence Graduates program.

View summaries of Round one Emerging Technologies programs.

AI in mental health

Almost half of Australians will experience mental ill health at some point in their lives, however nearly 70% of those with a known mental health condition do not seek professional help due to factors such as stigma, cost, limited access to care and fear of hospitalisation. Most mental health problems are present and established between the ages of 14-24, highlighting the importance of early detection and intervention.

Aside from the human cost of mental illness, the economic burden is considerable, with a reported $10.6 billion spent on Australian mental health services and reported productivity losses in industry due to mental ill health being as high as $39 billion per year. As such, improving the mental health of the Australian population is not only a significant societal need, but also a government priority that requires attention.

There are significant opportunities for AI to help in this domain, from facilitating data supported decision making in hospitals through to enabling self-directed intervention via everyday technologies.

We are proposing a cross-disciplinary cohort of 20 PhD and Masters students across Melbourne, Monash and Monash Malaysia (independently funded) Universities to explore innovative approaches towards AI driven methods and solutions, driven by real-world industry priorities.

Chief Investigators Dr Roisin McNaney, Associate Professor Marie Yap, Professor Jianfei Cai, Professor Mario Alvarez, Dr Simon D'Alfonso, Professor Vassilis Kostakos
Universities MonashMonash MalaysiaMelbourne
Industry partners Together AIMedi AIOutcome HealthTurning PointOrygenHeadspaceMonash HealthWMHCAmazon Web ServicesCSIRO
Student degree type PhD, Masters 1 year project (RTP eligible)

The Offramps Project: Finishing School Well

This proposal will grow a world-class cohort of graduates who will combine innovative Artificial Intelligence (AI) methods with individual case-based research to rapidly learn the causal pathways which help young people finish school well providing an off-ramp to social disadvantage.

The proposed research programme builds a broad partnership across the NSW government Department of Education, leading researchers in data science, from the University of Technology Sydney and CSIRO as well as leading researchers in the social sciences from the University of Western Sydney and the Australian National University, to train a multi-disciplinary team of AI savvy graduates equipped to use the latest advances in AI for social good.

This connectivity will enable students to build substantial new cross-disciplinary capacity and knowledge in data-driven discovery for social change. The goal is to draw the community of data science and domain science researchers together with government bodies to address the substantial and challenging problems posed by social disadvantage in the context of education.

Chief Investigators Professor Sally Cripps, Associate Professor Rebekah Grace, Dr Roman Marchant, Professor Fang Chen, Dr Melanie Loveridge, Associate Professor Ian Opperman
Universities Western Sydney, UTSANU
Industry partners Paul Ramsay FoundationNSW Department of EducationCSIRO
Student degree type PhD, Masters 1 year project (RTP eligible)

AI Enabled Advanced Materials Technology

This project will contribute to a critical and timely upskill in Australia's capability in applied artificial intelligence (AI) – firmly impacting the Science and Research Priority area of Advanced Manufacturing.

The proposal will train a PhD cohort, with world class supervision and industry partnership in two core areas; namely Machine Learning (ML) and Computer Vision (CV). Research students will participate in industry-led research projects and placements to build job-ready skills.

In the domain of ML, this project aims to develop and demonstrate new, reliable methods for materials design, combining machine learning with design thinking. The development of advanced materials has a long history in Australia, and unlike the design of other engineered products, there is no clear design methodology for materials to date. Exploiting ML will enable rapid design and development of alloys for specific applications – with sustainability in the design process.

In the domain of CV, advanced materials technology that includes (i) durability assessment of infrastructure (using robotic vision) and, (ii) integration of CV into the manufacturing process – are critical for enhanced operational efficiency and building emerging technology capability.

The development of technologies in the domain of materials is critical in meeting rapidly emerging requirements and sovereign societal needs.

Chief Investigators Professor Nick Birbilis, Professor Svetha Venkatesh, Professor Amanda S Barnard, Associate Professor Hanna Suominen, Dr Kevin J Laws, Dr Qing Wang
Universities DeakinANUUNSW
Industry partners BlueScopeDuratecAdvanced Alloy HoldingsMaple Glass PrintingNanoCubeCSIRO
Student degree type PhD, MPhil, Honours

Towards AI on the edge: Developing data-efficient machine learning models for multimodal sensing devices and IoT

The project aims to advance novel data-efficient machine learning techniques for modelling sensor data obtained by resource-constrained devices, such as microcontrollers, wearables, mobile devices, and IoT. These data are typically high in dimension, noise and uncertainty, and highly varied in its modality, purpose and tasks, ranging from end-consumer wearable sensors and devices capturing human activities and physiological signals to infrastructure sensors and IoT capturing large-scale urban flows and mobility patterns.

This program seeks to enable modelling of human behaviours more efficiently at the edge, with little or no labelled data. It expects to make major breakthroughs in the modelling of human behaviour through a generalised representation learning techniques for different downstream tasks and domains, including transportation and mobility, building construction and management, agriculture and mining, and health and productivity.

The program involves three Australian leading institutions, UNSW, Curtin, and ANU, with strong support from six industry partners: Aurecon, STSA (a subsidiary of SoftBank Japan), SAP, N2N.AI, and HugHealth. The program seeks to train a cohort of twelve students, including four PhD, one Master by Research, and seven Honours students, with the goal to train them on developing and deploying AI on the edge, and embed them in industry-relevant problems.

Chief Investigators Professor Flora Salim, Professor Tom Gedeon, Dr Piotr Koniusz
Universities UNSW, Curtin, ANU
Industry partners AureconSAPSTSA (SoftBank)N2N.AI, Hug HealthResearch Screener
Student degree type PhD, MPhil, Honours

All System analysis–analytics and intelligent automation

The aim of this program is to develop an ‘all system analysis’ program that provides post-graduate students with an authentic learning experience by adopting the cohort-based approach to solving real-world engineering problems, thereby enabling them to develop key transferrable skills necessary for future employment. The cohort is carefully designed to comprise 11 distinct but inter-related projects, covering virtually all use cases of data analytics, including to personalise user experience, inform business decision-making, streamline operations, mitigate risk and handle setbacks, enhance safety and security .

Further, the cohort project aims to link process control and machine performance with visualization and human-machine interaction and tackle the critical industrial objectives of developing hybrid man/machine systems where the machine response can be autonomous but benefit from human input.

Students in the cohort with strong math and data analytics backgrounds will be complemented by their peers with product design and manufacturing knowledge, and vice versa, which allow them to seamlessly transfer their abstract ideas from theoretical analyses to practical implementations. The common themes that the student cohort will be working on are related to the algorithm and prototype development, which will later be tested in individual industry partner’s environment.

Chief Investigators Professor Ivan Cole, Professor Xiaodong Li, Associate Professor Kate Fox, Dr Ehsan Asadi, Professor Spiridon Ivanov Penev, Dr Hamid Khayyam
Universities RMIT, UNSW
Industry partners  Downer EDI RailApplied Solar EnergyDelta-V ExpertsMemjet Australia, Memko Systems, PI NetworkQuaefacta HealthInergyYellowFIN Robotics Solutions
Student degree type PhD, MPhil

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