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November 2017
Message 20

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[Met-jobs] PhD Opportunity at the University of Oklahoma (USA)

From Roger Brugge <r.brugge@reading.ac.uk>
To "met-jobs@lists.reading.ac.uk" <met-jobs@lists.reading.ac.uk>
Date Tue, 7 Nov 2017 14:30:56 +0000

Forwarded from CLIMLIST...



PhD Opportunity in the School of Meteorology at the University of Oklahoma

Developing a Framework for Seamless Prediction of Sub- Seasonal to
Seasonal Extreme Precipitation Events in the United States

Position Description: Our research group seeks a motivated PhD student,
interested in a leadership role in determining the space and time
variations of extreme precipitation events. The project aims to enhance
fundamental understanding of the large-scale dynamics and forcing of
sub-seasonal to seasonal (S2S; 14 days to 3 months) extreme
precipitation events in the U.S. and improve our capability to model and
predict such events (i.e., precipitation that far exceeds climate norms
for a given period). The student will work with observational and
modeling subgroups in our team to identify extreme events in
nonstationary precipitation time series and gain an understanding of
novel machine-learning techniques in this endeavor using data from
high-resolution radar composites, and dynamical coupled climate models.
The ultimate deliverable from this project for the Ph.D. student will be
a flexible statistical framework with which to work with S2S extreme
precipitation events databases. In addition to the statistical and
meteorological elements of the work, the student will interdisciplinary
experience by engaging with interested stakeholders and applying the
results of this project to their specific needs in co- production of
knowledge. The skills gained will be highly marketable for both academic
and private-industry markets.

The ideal candidate will have: (1) strong computer programming skills in
an Unix environment (specific language flexible); (2) an understanding
of statistics, large-scale atmospheric and/or climate dynamics, and
synoptic-scale meteorology; (3) an intellectual curiosity to learn and
innovate in machine learning and statistical techniques related to S2S
prediction problems; and (4) strong scientific oral and writing skills.

Start Date: January 2018. Anticipated funds sufficient for 5 years of
continuous funding.


Interested students should contact both

Drs. Michael Richman (mrichman@ou.edu <mailto:mrichman@ou.edu>) and
Jason Furtado (jfurtado@ou.edu <mailto:jfurtado@ou.edu>). Please include
a copy of your CV, academic transcript, and short statement of interest.





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