1x full time, fixed term (19 month) position available
Salary Level at Academic Level B + 17% Super
Be part of multidisciplinary research teams, contributing to two projects funded by The Centre for New Energy Technologies (C4NET)
One by RMIT and CSIRO to develop data driven energy consumption model, and another in collaboration with Federation University, GWMWater and Gippsland Water to develop analytics for demand-response for water utility

About The College

The STEM College holds a leading position and expertise in the science, technology, engineering, mathematics and health (STEM) fields. We are uniquely positioned to influence and partner with industry, as never before.

STEM College is a community of exceptional STEM researchers, teachers, inventors, designers and game-changers, supported by talented professional staff. We offer higher education programs across all STEM disciplines at the Bachelor, Master and PhD levels, and ensure our students experience an education that is work-aligned and life-changing.

Further information on STEM College in this link .

About The Role

As the Research Fellow, you will work on two projects funded by the Centre for New Energy Technologies (C4NET). First project is co-funded by C4NET and CSIRO, with a particular focus on predicting precinct level energy use using data from buildings and other mobility patterns, leveraging Machine Learning (ML) and Deep Learning (DL) models. In this project, you will work on predicting precinct level energy use using data from buildings and other mobility patterns. A data driven energy consumption model that utilizes building data and mobility data will be developed. This model can inform energy use behaviour for optimally managing urban infrastructure during normal periods and extreme events.

The second C4NET project is co-funded by GWMWater and Gippsland Water, in collaboration with Federation University and will have you work on analysing energy use for demand response for optimising water utility operations.


RMIT University


Strong experience in at least one of the following areas: machine learning and deep learning and time-series and spatio-temporal data mining
Experience in developing and/or using state-of-the-art methods, software and tools in a relevant research context
Experience in conducting experiments as required in the relevant expertise and subfield
Strong writing skills and experience in preparing publications for a variety of audiences, including relevant publications in high quality Q1 peer reviewed journals and track records in A* conferences as ranked by CORE in AI, machine learning, and data mining, such as ICML, NeurIPS, ICLR, AAAI, KDD, WWW, WSDM, and other relevant A* conferences
Strong interest in the energy analytics domain
Well-developed oral and interpersonal skills and ability to communicate effectively with a wide range of stakeholders, including presentations at seminars, conferences and industry events
Optional: Emerging track record and recognition for high quality research engagement, including development of new research initiatives, competitive research funding, and industry links


Mandatory: Completion of a PhD in Computer Science or a relevant discipline area.

Educational level:

Ph. D.

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