CSIRO Industry PhD Scholarship: Physics-Informed and Explainable AI for Water Security: Uncertainty-Aware Modelling from Multisource Environmental Observations

Funding and duration

four year scholarship package totalling approximately $48,000 per annum tax exempt (2026 rate); four-year scholarship and research training program.

Project overview

Project title

Physics-Informed and Explainable AI for Water Security: Uncertainty-Aware Modelling from Multisource Environmental Observations.

Project description

This project integrates diverse environmental observations including in‑situ water‑quality measurements, flowdata, hyperspectral reflectance, and satellite imagery from platforms such as Sentinel and Landsat, accessed through CSIRO’s EASI environment. These rich datasets come from real‑world CSIRO AquaWatch pilots and Hunter Water Corporation, with the Williams River catchment and Grahamstown Dam serving as primary testbeds.

Supervisory team

University

Name of university supervisor | Ravinesh Deo

Name of university | University of Southern Queensland

Email address | ravinesh.deo@usq.edu.au

Faculty | School of Science, Engineering and Digital Technologies

CSIRO

Name of CSIRO supervisor | Warren Jin

Email address | warren.jin@csiro.au

CSIRO Research Unit | Technology

Industry

Name of industry supervisor | Prabal Barua

Name of organisation | Cogninet Australia Pty Ltd

Email address | prabal@cogninet.com.au

Further details

Primary location of student | University of Southern Queensland, 37 Sinnathamby Boulevard, Springfield Central QLD 4300, Australia

Industry engagement component location | Cogninet Australia Pty. Ltd, Level 5, 29-35 Bellevue St, Surry Hills NSW 2010, Australia

Other locations | CSIRO Black Mountain, Clunies Ross Street, Acton ACT 2601, Australia

Ideal student skillset | A strong interest in environmental modelling, artificial intelligence, and real‑world impact. A background in one or more of the following: machine learning, data science, environmental engineering, hydrology, physics‑based modelling, statistics, or remote sensing. Experience with Python, scientific computing, or geospatial data tools (e.g., GIS, satellite imagery processing) will help you hit the ground running, but curiosity and willingness to learn are just as important.Students who enjoy working with complex datasets, developing new algorithms, or exploring how physical principles can guide AI models will thrive in this project. An interest in explainable AI, uncertainty quantification, or environmental monitoring is a bonus. You will collaborate with researchers from CSIRO and university partners, gaining hands‑on experience with cutting‑edge environmental datasets and modelling platforms. This project suits students who want to build advanced technical skills while contributing to Australia’s long‑term water security.

Application close date | Open until position filled

Apply | Contact Ravinesh Deo