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University of Southern Queensland

PhD in Physics-Informed and Explainable AI for Water Security University of Southern Queensland in Australia

Degree Level

PhD

Field of study

Computer Science

Funding

Full funding available
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Country

Australia

University

University of Southern Queensland

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Keywords

Computer Science
Data Science
Environmental Science
Remote Sensing
Hydrology
Civil Engineering
Earth Science
Uncertainty Analysis
Explainable Ai
Ecological Modeling
Statistics
Geospatial Information
Physics
ML

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About this position

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

This PhD project combines environmental modelling, machine learning, data science, hydrology, remote sensing, and statistics to build AI methods for water-security applications. The work uses diverse environmental observations, including in-situ water-quality measurements, flow data, hyperspectral reflectance, and satellite imagery from Sentinel and Landsat, accessed through CSIRO’s EASI environment.

The project is linked to real-world CSIRO AquaWatch pilots and Hunter Water Corporation, with the Williams River catchment and Grahamstown Dam as primary testbeds. Students will collaborate with researchers from CSIRO, the University of Southern Queensland, and Cogninet Australia Pty Ltd, gaining hands-on experience with advanced environmental datasets and modelling platforms.

Primary location of the student is the University of Southern Queensland in Springfield Central, Queensland, Australia, with industry engagement at Cogninet Australia Pty Ltd in Surry Hills, New South Wales, Australia, and additional work at CSIRO Black Mountain in Acton, Australian Capital Territory, Australia.

Ideal applicants should have a strong interest in environmental modelling, artificial intelligence, and real-world impact. Helpful backgrounds include machine learning, data science, environmental engineering, hydrology, physics-based modelling, statistics, or remote sensing. Skills in Python, scientific computing, GIS, or satellite imagery processing are advantageous. Interest in explainable AI, uncertainty quantification, and environmental monitoring is a bonus.

The application is open until the position is filled. To apply, contact Ravinesh Deo by email.

Funding details

Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.

How to apply

Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.

More information can be found here

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