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Research Fellow Artificial Intelligence

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  • Are you looking for a three-year, fixed-term contract as a Research Fellow?
  • Can you provide expertise in explainable AI modelling and visualisation techniques?
  • Do you have an interest in achieving strategic collaborations for climate change modelling?

Mō Te Herenga Waka – About our university

Te Herenga Waka – Victoria University of Wellington is a global-civic university with our marae at our heart. This iho draws off our heritage and is further defined by our tūrangawaewae, in particular Wellington, Aotearoa, and the Asia-Pacific, all of which are expressed in our position as Aotearoa New Zealand’s globally ranked capital city university.

Kōrero mō te tūranga – About the role

Te Herenga Waka – Victoria University of Wellington is currently recruiting a Research Fellow to join the Antarctic Research Centre and the newly established Centre for Data Science and Artificial Intelligence on a three-year fixed-term contract. The successful applicant will provide expertise in artificial intelligence, particularly in machine learning, deep learning, explainable AI modelling and visualisation techniques. The Research Fellow will develop AI and machine learning modelling algorithms and tools to help the ARC achieve its goals by undertaking statistical projections of future ice sheet and ocean changes, investigating how the polar regions respond to future emissions trajectories over a range of timescales.

Key responsibilities:

  • Conceptualise and develop a machine learning framework to apply to Greenland and Antarctic ice sheet and ocean simulations.
  • Apply the trained model to Greenland and Antarctic ice sheet and ocean simulations to improve future projections.
  • Share and communicate results with other researchers.

As an expert in artificial intelligence, the successful applicant will work with our team of process modellers and a team in CDSAI to:

  • develop and train machine learning (ML) algorithms on satellite observational datasets, as well as on the outputs of process-based models to predict environmental outcomes under ‘unknown’ future scenarios
  • use in situ observations of ocean temperature, salinity, and ice-shelf melt rate as training data to produce an optimized melt scheme for process-based ice sheet/shelf models
  • employ the previously learned models with high-resolution ocean data, for example, to capture the influence of eddies in transfer heat across the continental shelf.

The experiments will be carried out on Grid-Computing and high-performing GPU resources, which CDSAI will provide.

Ō pūmanawa – About you

Do you have:

  • a relevant PhD or in physical sciences with a significant computational and computer programming component
  • demonstrated practical experience setting up and running machine learning/statistical models, preferably for physical science applications
  • proven skills in presenting results to a wide range of audiences, including peer-reviewed publications.

Please go to our careers site to view the role description.

Applications close on Friday, 1 September 2023.

Contact details for vacancy

If you have any questions regarding this role, please get in touch with Prof. Nicholas Golledge ([email protected]). But applicants should follow all steps listed below. 

Important – Application steps and information

Download and complete the university application form.  Then, please combine your cover letter, CV and the university application form into a single file as only one can be attached (preferably in pdf format). 

Click ‘Apply Now’ and follow the process to enter your details, attaching your combined file.  If you have any issues uploading your combined document, please email this to [email protected] stating the reference number and position title from the advert in the subject line.

Click Here to Apply For The Job



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