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

  • Priyanga Dilini Talagala, Rob J Hyndman, Kate Smith-Miles, Sevvandi Kandanaarachchi and Mario A Muñoz (2019) Anomaly detection in streaming nonstationary temporal data. Journal of Computational and Graphical Statistics, to appear. Abstract  pdf
  • George Athanasopoulos, Puwasala Gamakumara, Anastasios Panagiotelis, Rob J Hyndman and Mohamed Affan (2019) Hierarchical forecasting. Macroeconomic forecasting in the age of big data, ed. P. Fuleky, Chapter 23. Abstract  pdf
  • Earo Wang, Di Cook and Rob J Hyndman (2019) A new tidy data structure to support exploration and modeling of temporal data. Abstract  pdf
  • Priyanga Dilini Talagala, Rob J Hyndman, Catherine Leigh, Kerrie Mengersen and Kate Smith-Miles (2019) A feature-based framework for detecting technical outliers in water-quality data from in situ sensors. Abstract  pdf

Recent and upcoming seminars

  • High-dimensional time series analysis. (17 August 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...
  • Seasonal functional autoregressive models. (9 December 2018) More info...