Overview

About the project

Rapid advancements in multisource satellite remote sensing, including missions like GRACE (Gravity Recovery and Climate Experiment) and SWOT (Surface Water Ocean Topography), present unprecedented opportunities to enhance observations and predictive capabilities for various hydrologic processes. SWOT monitors surface water bodies such as oceans, rivers, and lakes, offering precise measurements of water levels and flow dynamics, while GRACE tracks changes in Earth's gravity field to monitor terrestrial water storage variations, encompassing groundwater depletion and ice mass loss.

However, the integration of multiple satellite data products, particularly SWOT and GRACE, remains relatively unexplored in addressing hydrologic challenges, representing an emerging frontier in both hydrological research and satellite remote sensing.

The Integration of Swot and Grace Satellite Data for Hydro-climatic Analysis Using Advanced Machine Learning Techniques project is offered at Deakin University.

Programme Structure

By assimilating SWOT and GRACE datasets, we aim to provide accurate assessments of different hydrologic extremes (e.g., floods, droughts), water availability, and distribution, providing valuable insights for water resource management and climate change adaptation strategies.
This could be achieved by harnessing the advantages of advanced machine and deep learning techniques such as transformer-based models. These architectures can effectively capture the spatial and temporal dependencies in multi-source satellite data, extracting relevant features and making accurate predictions about future hydrological conditions.

Key information

Duration

Full-time
- 48 months

Start dates & application deadlines

Starting 2025-07-01 00:00:00
- Apply before 2025-04-30 00:00:00

Language

English

Delivered

On Campus

Disciplines

Artificial Intelligence Machine Learning View 17 other PhDs in Artificial Intelligence in Australia

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Student insurance

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Additional medical costs (i.e. dental)
Repatriation, if something happens to you or your family
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Home contents and baggage
Accidents
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Other requirements

General requirements

academic transcripts

curriculum vitae (CV) detailing publications, relevant professional and research experience
evidence of English language proficiency
statement of purpose (a written statement of your motivation)

contact details of two academic referees.
CRICOS Provider Code: 00113B

Tuition Fee

To always see correct tuition fees

International

Free

Tuition Fee

Based on the tuition of 0 AUD for the full programme during 48 months.
National

Free

Tuition Fee

Based on the tuition of 0 AUD for the full programme during 48 months.

All successful applicants will receive a scholarship that will cover tuition fees, a stipend and financial support.

Living costs for Geelong

1000 - 2000 AUD /month

Living costs

The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.

Funding

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Scholarships Information

Below you will find PhD's scholarship opportunities for Integration of Swot and Grace Satellite Data for Hydro-climatic Analysis Using Advanced Machine Learning Techniques.

Available Scholarships

You are eligible to apply for these scholarships but a selection process will still be applied by the provider.

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