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March

Research Fellow / Senior Research Fellow - Deep Learning

THE UNIVERSITY OF WESTERN AUSTRALIA - Perth, WA

Any Industry
Source: uWorkin

Source: uWorkin

JOB DESCRIPTION

Job no:506486
Work type:full time
Location:Crawley
Categories:Engineering and Mathematical Sciences

  • Applications open to researcher with academic expertise or industry experience
  • Full time and 1 year fixed term appointment 
  • Salary range: Level B/C, $102,983 - $144,523 per annum plus Superannuation

Oceans Graduate School

The University of Western Australia (UWA) is ranked amongst the top 100 universities in the world and a member of the prestigious Australian Group of Eight research-intensive universities. With an enviable research track record, vibrant campus and working environments, supported by the freedom to ‘innovate and inspire’ there is no better time to join Western Australia’s top University.

About the team

The Oceans Graduate School (OGS) engages in fundamental and applied research to find solutions for the critical issues facing our oceans, coasts and estuaries, while also supporting industries that work in the marine environment. Our school operates state-of-the-art facilities around Western Australia used for world-class marine research, and engages with partners in Australia and around the world, including other universities, governments and research institutions, so more can be done to better our oceans and ensure an economically viable and environmentally sustainable future for our population.

About the project

Ocean current forecasts are required to support safe offshore operations. However, the underlying numerical models are sensitive to assumed initial and boundary conditions, and may also be impacted by atmospheric forcing, numerical discretisation techniques, and parametrisations of key processes. This results in forecasts that may be too inaccurate for operational decision making.

With the speed up in deep learning approaches, and their relative ease of application in cases where large data sets exist, the opportunity may exist to use such techniques to deliver a deep learning integrator that can forecast currents more accurately than existing physics based numerical modelling approaches.

The objective of this research project is to investigate, prove and develop a deep learning based ocean current forecasting model that leverages classical and state of the art ocean current forecasting inputs from multiple (ensemble) sources / models, as well as available earth observations routinely acquired (such as remotely sensed ocean colour and sea surface temperature) and other regional data (such as ocean drifters) where available.

This project extends the close collaboration between the OGS and Shell Australia (Shell). Shell will engage closely throughout the project, and will provide a key role in the provision of the data sets needed for the research. If successful, Shell would seek to deploy the developed model in support of their offshore operations.

Why the Oceans Graduate School at The University of Western Australia

Working in the OGS (and being based at UWA) will provide you an opportunity to engage with a wide range of researchers, with varying interest areas. You will be encouraged to interact widely and participate actively in the research activities of the school.

About the opportunity

As the appointee, you will be involved in all major aspects of the project, with your specific role being to lead the development of a deep learning approach to accurately forecast ocean currents. In this position you will work closely with a highly experienced, dynamic and supporting team of oceanographers based at UWA. 

You will be provided with extensive data sets to support the research, including ocean current data from global/local numerical model solutions, remotely-sensed observations from satellite and in-situ observations. In addition, you will have access to other ocean data including waves, wind and meteorological observations.

Reporting and presentation of the work will be undertaken at regular intervals, and additional phases may be agreed depending on success.

To be considered for this role, you will demonstrate:

  • Hold a relevant tertiary qualification with a physical sciences background, or equivalent industry experience developing deep learning algorithms to address applied problems
  • Expertise and/or extensive experience in the development of deep learning (or other applicable spatio-temporal machine learning) methods to tackle applied problems.
  • Strong computational skills (ideally with experience in high-performance computing and/or cloud computing) with the ability to organise and process large and diverse data sets.
  • Highly developed interpersonal, verbal and written communication skills with the ability to work effectively as part of a team.
  • The ability to work independently and show initiative

Full details of the position responsibilities and the selection criteria are outlined in the position description. In preparing your application you are asked to submit curriculum vitae and a document clearly addressing the selection criteria to demonstrate that you meet the criteria. 

Please see the position description prior to applying: 506486.pdf

Closing date: Tuesday, 13 April 2021

This position is only open to applicants with relevant rights to work in Australia.

Application Details: Please apply online via the Apply Now button.

Our commitment to inclusion and diversity

UWA is committed to a diverse workforce. We celebrate inclusion and diversity and believe gender equity is fundamental to achieving our goal of being a top 50 university by 2050.

  • We have child friendly areas on campus, including childcare facilities. Flexible work arrangements, part-time hours and job sharing will all be considered.
  • UWA has been awarded Platinum Employer Status for being a Top Ten Employer for LGBTI Inclusion by the Australian Workplace Equity Index (AWEI -2019).
  • UWA has also been awarded Athena Swan Bronze Accreditation in 2020.

Advertised: W. Australia Standard Time
Applications close: W. Australia Standard Time