Postdoctoral position in Statistical Data Science on Ensemble
Postprocessing for High-impact Events
In the framework of a collaboration being currently set up between the
University of Bern and the Swiss Federal Office of Meteorology and
Climatology (Meteoswiss), we are calling for applications for a 2-year
postdoctoral position (80%-100%) to be funded subject to successful
completion of the collaboration contract.
The main goal is to investigate and develop statistical postprocessing
methods for improving the forecasts of meterological parameters related
to severe weather events and corresponding weather warnings (such as
precipitation, wind, temperature). These high-impact weather events are
not strictly extreme events, but relatively rare and postprocessing
methods for general weather may no longer be optimal in this context.
The desired focus on rare events will be achieved by suitable
adaptations of scoring rules. Besides this, parametric, semi- and
non-parametric distributional probabilistic forecasting methods will be
compared, possibly including linear and non-linear features within the
covariates. When applicable, Machine Learning approaches will be
included in benchmarks and also possibly leveraged within statistical
postprocessing. Also, since non-parametric approaches may call for more
data than available locally, spatial weighting schemes will be
considered, with a view towards methods of analogs for designing
efficient weighting.
The recruited postdoctoral researcher will be mentored from the academic
side by a multidisciplinary team from Statistics (Prof. David
Ginsbourger and Prof. Johanna Ziegel) and Geography (Prof. Olivia
Romppainen-Martius and Dr. Pascal Horton), all affilliated with the
Oeschger Center for Climate Change Research (University of Bern). The
recruited Postdoc will also work a substantial part of her/his time at
MeteoSwiss (near Zürich) under the guidance of Dr. Jonas Bhend and Dr.
Mark Liniger. This will ensure an optimal know-how transfer on the
understanding of the movivating problems, meteorological expertise, the
exact user needs and practical applicability and the work can build upon
prior experience at MeteoSwiss.
The ideal candidate either has a PhD in statistics with a strong taste
for applications in areas related to meteorology and climate sciences,
or a PhD in climate sciences with a strong background in theoretical and
applied statistics. In all cases, the position will require outstanding
data analysis and implementation skills, very good communication and
team player abilities, and academic writing proficiency. We offer a
multi-disciplinary environment with the possibility to have an impact on
science and society. The starting date is as soon as possible in 2020.
Applications will be reviewed swiftly and selected candidates will be
contacted for interviews.
CV, motivation letter and references to be sent jointly to
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