[ES_JOBS_NET] NSF-funded postdoc: Statistical methods + temperature extremes

[Karen McKinnon]

The McKinnon group <https://karenamckinnon.github.io/> at the University of
California, Los Angeles (UCLA) is seeking a postdoctoral scholar to develop
and apply novel statistical and machine learning methods towards the goal
of modeling and predicting temperature variability and extremes. We are
particularly interested in quantifying the relative roles of the atmosphere
and land surface in causing or modifying the statistics of daily
temperature. The postdoctoral scholar will work closely with both Professor
McKinnon and Dr. Isla Simpson at the National Center for Atmospheric
Research (NCAR) to analyze large and diverse datasets, including station
data, reanalyses, and climate model output. The selected applicant will
also work in parallel with Wenwen Kong, a current postdoctoral scholar
using idealized climate model simulations as a tool to explore these same
science questions. The position is funded in part by the National Science
Foundation.

While we prefer that the selected candidate will join us in-person at UCLA
once travel and relocation is possible, we are open to discussing remote
work. We will support conference travel, travel to collaborate in-person
with Dr. Simpson in Boulder, and other professional development. The
successful candidate would join an active community of postdocs at UCLA;
see https://www.postdoc.ucla.edu/ for resources and information.

Responsibilities:

   -

   Develop descriptive and predictive statistical models for temperature
   variability and extremes
   -

   Integrate observationally-based analyses with physical insights from a
   hierarchy of climate models
   -

   Publish results in high-quality, peer-reviewed journals
   -

   Present results at conferences and seminars


Minimum qualifications:

   -

   PhD in atmospheric sciences or related field
   -

   Experience with statistical modeling and/or machine learning
   -

   Excellent written and oral communication skills
   -

   Proficiency in python (preferred), Matlab, or other data analysis
   software
   -

   Ability and desire to pursue research both independently and as part of
   a team


Preferred qualifications:

   -

   Fluency in python
   -

   Substantial coursework and/or research experience in statistical modeling


The initial appointment will be for a 12 month period, with the possibility
of renewal for an additional 12 months subject to satisfactory performance.
Salary will be commensurate with experience.

To apply, please submit a cover letter explaining your interests and
relevant qualifications, a current CV, and contact information for three
references through UCLA Recruit, https://recruit.apo.ucla.edu/JPF06365.
Only references for shortlisted candidates will be contacted. Applications
will be accepted until filled, however to ensure full consideration,
applications must be submitted by 11:59pm on May 19, 2021. The selected
candidate can begin the position in the summer or fall.

Questions about the position may be directed to Karen McKinnon (
kmckinnon at ucla.edu).


-- 
Karen McKinnon
Assistant Professor of Statistics and the Environment
University of California, Los Angeles
Mathematical Sciences Building 8967
https://karenamckinnon.github.io/
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