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<p class=""><font face="Calibri" size="3" class=""><u class=""><span class="" style="font-weight:700">Position available in </span><b class="">January</b><span class="" style="font-weight:700"> 2018</span></u></font></p>
<p class=""><span class="" style="font-size:12pt; font-family:Calibri; font-weight:700">Position Description</span><span class="" style="font-size:12pt; font-family:Calibri">: 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.</span></p>
<p class=""><span class="" style="font-size:12.000000pt; font-family:'Calibri'">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.
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<p class=""><span class="" style="font-size:12.000000pt; font-family:'Calibri'; font-weight:700">Start Date</span><span class="" style="font-size:12.000000pt; font-family:'Calibri'">: January 2018. Anticipated funds sufficient for 5 years of continuous funding. </span></p>
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<div class="">Apologies for cross-posting.</div>
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<div class="" style="orphans:2; widows:2">*******************************************</div>
<div class="" style="orphans:2; widows:2">Dr. Elinor Martin<br class="">
Assistant Professor</div>
<div class="" style="orphans:2; widows:2">School of Meteorology<br class="">
University of Oklahoma</div>
<div class="" style="orphans:2; widows:2"><span class="" style="orphans:auto; widows:auto">120 David L. Boren Blvd. Suite 5900</span></div>
<div class="" style="orphans:2; widows:2">Norman, OK 73072-7307</div>
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<div class="" style="orphans:2; widows:2">Office: NWC 5642</div>
<div class="" style="orphans:2; widows:2">Email: <a href="mailto:elinor.martin@ou.edu" class="">
elinor.martin@ou.edu</a></div>
<div class="" style="orphans:2; widows:2">Phone: 405 325 7392</div>
<div class="" style="orphans:2; widows:2">Web: <a href="http://weather.ou.edu/~ermartin" class="">
http://weather.ou.edu/~ermartin</a></div>
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