Proposed ARC ITTC Training Centre
Water expertise, AI research and hands-on training. Together, helping teams make decisions they can trust.
We are preparing an ARC Industrial Transformation Training Centre proposal. These dates help partners plan their involvement.
Expressions of interest close 23 October 2026
Register your Expression of Interest to be a Partner Organisation or an AI Training Partner Organisation in the ARC ITTC Training Centre AI for Water.
The form lets you download, edit and sign the EOI documents, then upload them. You can also leave questions for our team.
Register interest (opens in a new tab)
Scan to open the EOI form
1-2 pm AEDT
Join the QUT x WaterRA online Q&A for industry partners. Bring your colleagues and questions.
Register for the Q&A (opens in a new tab)WaterRA manages registration. You may need to log in or create an account.
Presentation recording
Watch the industry webinar on the Australian Water School website, including the Centre proposal and water-sector AI discussion.
Watch the recording (opens in a new tab)Water teams need to keep services reliable while renewing ageing assets, responding to changing weather and making limited budgets stretch further.
Experienced operators carry local knowledge that is hard to replace. Meanwhile, AI is moving beyond finding information to working through tasks and testing ideas. That creates possibilities, but it also raises questions about skills, safeguards and responsibility.
Our proposed Centre brings water science, AI research and workforce training into the same program. The aim is practical: help people use better evidence, test improvements carefully and build the capability to adopt AI well.
Experienced operators carry years of local knowledge. We want to capture that experience, check it against current evidence and make it easier to use.
How do we keep good judgement when people or conditions change?
Sensors, records and models rarely tell the whole story. AI needs to recognise gaps, respect water-system physics and ask for help when it is uncertain.
What do we need to know before we act?
Energy, treatment quality and resource recovery all matter. New approaches need controlled tests and evidence before they reach operations.
Can an improvement hold up outside the laboratory?
Tools change work. Teams need the skills to challenge an AI recommendation, understand who is accountable and keep their own expertise sharp.
How do we build confidence without losing capability?
The proposed program
We will connect technical research, workforce capability and assurance through real water-sector trials. Each theme informs the others.
Theme 1: AI-native water systems
Build systems that can use trusted knowledge, reason through a problem and test new ideas within clear limits.
What this produces: reusable knowledge tools, tested AI methods and evidence for operational decisions.
Theme 2: People
Study how operators, engineers and managers work with AI, then use the findings to improve role-based training.
What this produces: a tested competency framework and a curriculum that improves with the research.
Theme 3: Trust
Bring technical assurance together with accountability, cybersecurity, community expectations and the economics of adoption.
What this produces: a water-AI readiness and assurance framework partners can use before moving ahead.
Theme 4: Living labs
Work with partners on real systems, with staged testing and operators involved throughout.
What this produces: field evidence, useful case studies and a clearer decision about adoption.
We call this earned autonomy. An AI system gets more responsibility only when its evidence, people and operating conditions support it.
L0: Inform
AI retrieves relevant procedures and records, checks their quality and flags gaps. A person interprets the information and decides what to do.
Example: locating the current operating procedure and checking sensor records.
Does it work within the tested limits?
Can the team use it and intervene?
Is its use authorised, safe and accountable?
Does the evidence hold in this setting?
A conceptual research framework, not a promise that every system reaches L4. Readiness depends on the asset and decision. Unsupervised safety-critical control is outside the proposed scope.
Partners would help choose the questions and sites. These proposed living-lab settings bring all four themes together.
Test AI-assisted treatment decisions and controlled bioreactor experiments, with energy and treatment performance considered together.
A partner question: can we reduce energy use while maintaining treatment quality?
Connect changing source conditions with current treatment knowledge and decisions people can check.
A partner question: when should the system ask for more evidence or an expert review?
Explore affordable sensing, local AI and knowledge support for smaller teams and less-connected systems.
A partner question: what is the minimum useful sensing for a dependable decision?
Study how monitoring, models and controlled trials can improve water management in changing natural systems.
A partner question: what can a well-monitored site teach us about a site with fewer observations?
AI-generated proposed scenes, not photographs of existing Centre facilities. The equipment illustrates possibilities, not validated designs or approved deployments. Sites, projects and operating permissions would be agreed with partners.
Learning that keeps pace
Every year of the proposed five-year program, research findings will update the training. What partners learn at work will shape the next research questions.
Partner challenges at the centre
Investigate partner questions in the lab and living labs.
Turn verified findings into role-based learning and practice.
Partner teams and PhD researchers on placement test AI adoption at work.
Check capability, evidence and safeguards. Identify the next questions.
Research brings new evidence into training. Training and partner experience bring new questions into research.
The proposed program combines technical water learning with AI, cybersecurity and human oversight. Monthly global seminars and an annual research update connect partners with current practice.
Start with the capability or challenge that matters to your organisation. We will help you choose a suitable pathway.
Build skills for safe, useful AI adoption. Delivery ranges from shared online learning to a tailored program for your organisation.
Co-design research around an operational, engineering, design or business challenge. Work with researchers to build and test an approach.
| Starting point | Cash / year | Included delivery |
|---|---|---|
| Shared Foundations | A$10k | Shared online learning, monthly global seminars and the annual research update. |
| Applied Team Training | A$20k | Shared Foundations, plus one on-site partner program each year. |
| Integrated AI Adoption Program | A$50k | Shared learning, with the annual on-site session replaced by a tailored two-day program. |
| Training-to-Research Partnership | A$100k | Everything above, expanded to tailored multi-session delivery twice a year and connected to co-designed research. |
All levels combine technical water and AI/workforce training. Participant numbers and the training package are agreed at each level. The agreed training package is included in the contribution.
Partner contributions may come from learning and development, IT/OT, operational improvement, capital delivery, or research budgets. The Centre and its delivery are proposed and subject to funding and partner agreements.
Discuss a partnershipOur proposed team connects water engineering, physical AI, training, governance and adoption across the four themes. Kathy can help you find the right conversation.
Professor, QUT
Proposed Centre Director
AI Water Governance
Setting the Centre's direction and helping water teams use AI responsibly.
View profile for Nick Ashbolt (opens in a new tab)
Associate Professor, Griffith University
Technical & AI Water Training
Helping operators build practical water and AI skills they can use with confidence.
View profile for Helen Stratton (opens in a new tab)
Professor, QUT
Physical AI Intelligence
Connecting robotics and AI to careful experiments in real physical systems.
View profile for Jonathan Roberts (opens in a new tab)
Professor, QUT
Water Engineering & Discovery
Turning water-process questions into research and new understanding.
View profile for Yang Liu (opens in a new tab)
Professor, QUT
Water AI & Security
Building trustworthy AI and protecting the systems and information it relies on.
View profile for Clinton Fookes (opens in a new tab)
Professor, QUT
AI Economy & Adoption
Helping organisations understand AI's value and put it to work in everyday practice.
View profile for Marek Kowalkiewicz (opens in a new tab)
Lecturer, QUT
Design, Operate & Field Trial
Taking ideas from water-system design into operation and partner field trials.
View profile for Luke Shi (opens in a new tab)
Associate Professor, UNSW
Stormwater AI Future
Exploring how AI can help us understand stormwater and shape more resilient cities.
View profile for Kefeng Zhang (opens in a new tab)
QUT
Industry Engagement
Your first contact for training, partnership and next steps.
Email KathyTell us about a capability gap, a difficult decision or a process you want to improve. We can explore where training or research would help.
Discuss a partnership