Post-docs in wind and solar power forecasting

We currently have two postdoc opportunities together with an industry partner in the field of wind and solar power forecasting (full time, Level B). They are suitable for recently graduated PhD students that can start between now and June-July. The opportunities are as follows: Wind power forecasting: 1 year contract Good programming skills in R and/or Python Solid background in Machine Learning and/or Statistics Background in time series forecasting desirable Solar power forecasting: 6 months contract Good programming skills in R and/or Python Solid background in Machine Learning and/or Statistics Data will be cloud coverage data from sky cams, so some image processing background is necessary Background in time series forecasting desirable Please contact Christoph Bergmeir if you are interested.

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Advice to PhD applicants

For students who are interested in doing a PhD at Monash under my supervision. First, check that you satisfy the following criteria: You must have completed a degree in statistics that involved some research component (e.g., an honours or masters thesis). A degree in computer science, mathematics or econometrics might be acceptable if it contained a substantial amount of statistics. A degree in any other field is not sufficient background to work with me.

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Recent publications

  • Pablo Montero-Manso, George Athanasopoulos, Rob J Hyndman, Thiyanga S Talagala (2019) FFORMA: Feature-based Forecast Model Averaging. International Journal of Forecasting, to appear. Abstract  pdf
  • Insha Ullah, Kerrie Mengersen, Rob J Hyndman, James McGree (2019) Detection of cybersecurity attacks through analysis of web browsing activities using principal component analysis. Abstract  pdf
  • Rob J Hyndman (2019) A brief history of forecasting competitions. International Journal of Forecasting, to appear. Abstract  pdf
  • Yanfei Kang, Rob J Hyndman, Feng Li (2019) GRATIS: GeneRAting TIme Series with diverse and controllable characteristics. Abstract  pdf

Recent and upcoming seminars

  • High-dimensional time series analysis. (17 August 2019) More info...
  • Developing good research habits. (20 March 2019) More info...
  • Feature-based forecasting algorithms for large collections of time series. (25 January 2019) More info...
  • Data visualization for functional time series. (11 December 2018) More info...