The Bureau's Research Program delivers world-class research aligned with the Bureau Strategy and the Research and Development Plan. It enables the development of weather and climate applications for the needs of the Australian community, supporting the Bureau's operational services, and advancing meteorological and earth system science in Australia and globally.
The ML Engineering Scientist will be a member of the Next Generation Modelling Team within the Earth System Modelling Section of the Research Program. This team is responsible for developing and delivering advanced computing infrastructure to support the program's portfolio of research and development activities. This specific data scientist role will focus on machine learning (ML) to advance the Bureau's global model forecast systems.
Advances in ML are transforming weather prediction, offering opportunities to improve weather forecast accuracy and efficiency. Deep learning ML models are showing increasingly competitive performance compared to traditional physics-based numerical weather prediction systems. This role will deliver the specialised expertise required to train and scientifically evaluate ML forecast models for Australia's forecast purposes, as well as optimising the related data pipelines, model configurations, and compute environments. The person will work with a small team of software engineers and scientists across the Next Generation Modelling and Coupled Modelling Teams, to focus on the training and scientific evaluation of global and regional ML forecast models. We are looking for a highly motivated person, who is keen on learning and extending their experience. Through training and on-the-job learning, you will become an expert in the development and evaluation of AI/ML based weather models on the Bureau's HPC infrastructure.
The key duties of the position include:
The responsibilities of the role include but are not limited to:
Eligibility
A degree or diploma of an Australian educational institution, or a comparable overseas qualification, which is appropriate to the duties; OR other comparable qualifications, which are appropriate to the duties.
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