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Conservation is a noisy business: modelling the effects of stochasticity on wildlife management decisions

To conserve species in disturbed natural environments, we need to use mathematical models to predict the consequences of different interventions. Unfortunately, these models are based on partial information of complex systems, and the systems themselves are subject to substantial observational and process noise.We often use ordinary differential equations to describe ecosystems, like the classic logistic growth model:dn/dt = r n (1 - n / k)However, these models are deterministic, and they assume we know the values of the key parameters …

Study level
Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science
Centre for the Environment

AI for wildlife conservation

Automated detection of animals using machine learning (AI) from drones and camera traps is creating powerful new opportunities for ecological research. Working as part of a team you will learn how to enable and use automated ID in imagery, use these tools to answer questions around wildlife abundance and interactions and help to shape new research directions. We work with a diverse range of species and you may have the opportunity to work on koalas, kangaroos and other macropods, lions, …

Study level
Master of Philosophy, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Biology and Environmental Science
Research centre(s)

Centre for the Environment

Chemicals that kill wildlife: an international and comparative study of Australia's approach to regulating the use and production of harmful chemicals

Certain chemicals like atrazine are well known to be very harmful to frogs, and a wide range of other wildlife and invertebrate species. Many countries around the world, except Australia, have banned the use of atrazine because of its harmful effects on important ecosystem markers like frogs.In this project we will first seek to identify chemicals that have been banned by most developed countries but not Australia. We will then identify and assess the regulatory and governance arrangements around atrazine …

Study level
Vacation research experience scheme
Faculty
Faculty of Business and Law
School
School of Law

Machine learning for wildlife monitoring

This project will investigate methods to monitor wildlife using machine learning applied to aerial imagery.While it is highly desirable to use drones and aerial footage to monitor wildlife, there are substantial challenges created by the nature of the data and target wildlife.This, combined with the vast nature of any collected aerial data, makes manual analysis difficult. This challenge motivates the development of machine learning methods to automatically process data and perform tasks, such as:detecting target animalscounting herd animalsclassifying land useassessing …

Study level
Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Data Science

Vegetation modelling for wildlife conservation and bushfire monitoring

Modelling vegetation using video captured from drones is a valuable component of conservation. Working as part of a team you will use existing imagery to create detailed vegetation models for ecological analysis. You will then use these models to address important questions around the impacts of bushfires and interactions between vegetation and wildlife.

Study level
PhD, Master of Philosophy, Vacation research experience scheme
Faculty
Faculty of Science
School
School of Biology and Environmental Science
Research centre(s)

Centre for the Environment

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