Student Opportunities

Post graduate students make up a core part of the Institute for Water Futures team.

Currently, there are no post graduate projects available. However, please see below for some our past project outlines to see if the work we do is right for you.

Top up scholarships of up to AUD$10k per year of study are available for some projects. Contact us at waterfutures@anu.edu.au for more information on these scholarships or upcoming projects.

For more information about AGRTP and application information, see the ANU scholarship information page here.

Example PhD: Uncertainty assessment of ecohydrological modelling and forecasting

Commencement date: CLOSED
Academic contacts: Baihua Fu (ANU), Carmel Pollino (CSIRO), Danial Stratford (CSIRO), Serena Hamilton (ANU), with government or private industry partners, depending on the case study selected.
Description: Ecohydrological modelling and forecasting have been increasingly used to support policy and decision making in environmental flows and water security. However, uncertainty in ecohydrological models and their forecasts are often poorly understood and rarely assessed. The sources and levels of uncertainty vary greatly depending on a range of factors such as how ecohydrological processes are represented in the models, data availability, the time horizons of forecasting, and how the models are used for decision making. This topic provides an opportunity to learn about uncertainty, risk assessment and the communication of uncertainty in water resource applications. It also provides an opportunity to work with leading ecological modelling experts in CSIRO.
Pre-requisites: Experience or a background in environmental modelling is essential. Experience in the water sector is highly desirable.

 

Example PhD or MPhil: Analysis of uncertainty management *top-up scholarship may be available

Commencement date: CLOSED
Supervisory/research team: Joseph Guillaume, with collaboration in the IWF and with government or private industry partners, depending on the case study selected
Description: As modelling and algorithm plays an growing role in decision making for both government and the private sector, uncertainty has become an increasingly prominent topic. There are a wide variety of ways of addressing uncertainty, but scientists, analysts and decision makers are not always able to choose the most appropriate ones. Projects would examine how uncertainty is currently managed in specific case studies and possible improvements. This topic provides an opportunity to learn about both current and cutting edge uncertainty management. Through collaboration, it also provides an opportunity to help understand and shape future decision making.
Pre-requisites: While methods can be tailored to the case, an inclination towards or experience with qualitative research methods and content analysis is desirable. Experience with argument analysis or discourse analysis is also a plus.

 

Example PhD: High resolution soil moisture estimation *top-up scholarship may be available

Commencement date: CLOSED
Supervisory/research team: Dr Luigi Renzullo, Dr Siyuan Tian
Description: An accurate assessment of plant available water is one of the many important information products for farmer decision support, especially around water allocation in advance of growing season or impending drought conditions. Satellite sources of soil moisture are limited in the information they provide, but it has been demonstrated that they are most useful when assimilated into water balance models (as either stand-alone or when integrated as part of crop-growth models). To further increase their utility, downscaling methods are applied to the satellite data to make the data more suited to the scale of farmer-scale decision support. This project explores various approaches for downscaling coarse resolution satellite soil moisture products for integration into crop-growth models. Downscaling methods based on fusion of optical and/or radar with passive microwave remote sensing will be a particular focus.
Pre-requisites: Potential students should have an interest in, preferably experience with, satellite soil moisture products gridded rainfall, climate and meteorological data using high performance computing tools. Students will develop, or possess, core computing skills (e.g. Python, R, CDO) in the processing and analysis of Earth observation, and gridded climate and weather data. Students will learn about the pros and cons of various optical, radar and microwave data, as well as explore data assimilation methods for plant available water estimation.

 

For more information about AGRTP and application information, see the ANU scholarship information page here.