Programmer at UC Irvine in Scientific Computing/Data Analysis

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Programmer at UC Irvine in Scientific Computing/Data Analysis

Our group at the University of California, Irvine has an opening
for a part-time (75%) scientific programmer to support externally
funded projects in climate modeling and data analysis. Contingent on
performance and funding availability, this position may later be
converted to full-time. Applicant review begins March 8, 2018.

Please apply to Job # 2018-0203 at


Programmer at UC Irvine in Scientific Computing/Data Analysis

The incumbent will provide services to the University in support of
grants awarded to the Climate and Scientific Computing group in the
Department of Earth System Science led by Dr. Charles Zender.
The incumbent will apply advanced computing techniques to problems
in global climate modeling and in scientific data processing.
The successful candidate will improve robustness, optimize, document,
and extend features of the netCDF Operators (NCO,, a
toolkit written in C/C++ and ANTLR; improve geospatial features and
parallelism to advance DOE and NASA projects; and update and maintain
a web site and computer platforms.

Skills, Knowledge and Abilities
BS or equivalent in a computation or climate- related discipline,
strong skills in libre C/C++/Python development (Autoconf, GCC,
GitHub) and in written and verbal communication. Expertise with
planar trigonometry. Ability to work independently to accomplish
project goals.

Desired: MS or PhD, familiarity with data standards (netCDF, CF),
geospatial tools (WKT, GEOS, PostGIS, xarray), and parallel
programming and message passing (OpenMP, MPI). Expertise with
spherical trigonmetry and with KD-Tree search algorithms.
Skill with website development (WordPress).

First Essential Function
Percent of Time: 50%
Incumbent will complete a regridding algorithm in C/C++ with OpenMP
that transforms datasets between arbitrary unstructured grids and
rectangular latitude/longitude grids while exactly conserving area.
The exact mathematical algorithm will be provided and needs to be
implemented and optimized with KD-Tree search algorithms for OpenMP in
a C/C++ framework.

Second Essential Function
Percent of Time: 30%
Incumbent will transition NCO regression tests from Perl-based to
Python-based and to a CMake/CTest environment.

Third Essential Function
Percent of Time: 20%
Incumbent will manage and improve NCO and group Web Site.

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