Research Associate – Machine Learning (underwater Sound) at The University of Western Australia (Perth)

Job Description

You will develop and apply novel approaches and machine learning techniques for the detection, classification and identification of marine fauna from passive acoustic monitoring (PAM) validated with paired visual identification data. You will be responsible for the development of techniques to generate datasets for species identification and ecosystem health, which can then be applied to existing datasets from Australia and internationally.

You will work closely with research staff across UWA and AIMS, and be expected to develop your own area of research interest to support the core science in collaboration with teams across AIMS and UWA. You will also have the opportunity to acquire new data sets in the field.

About you

You will provide evidence of published manuscripts and conference presentations with excellent written and verbal communication skills. You will demonstrate willingness to supervise and guide research students

To be considered for this role, you will demonstrate:

A PhD in passive acoustic monitoring (PAM), ecoacoustics, data science, machine learning or other related disciplines with a strong quantitative background

Experience in programming with scientific languages such as Python, Malab, or R

Experience in working with, and analysing large datasets

Experience with the use of machine learning techniques for use with sound data

Experience working independently and productively in multidisciplinary teams on a range of projects

Note: A current ‘C’ class driver’s licence is a requirement of this position.

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