Job announcement for one Photochemical Modeler at Tennessee Air Pollution Control Division.
Nashville, TN
Application deadline: August 31, 2022
The Division of Air Pollution Control Modeling team applies advanced air
quality models to simulate the transport and fate of pollutants. The Division
is seeking a photochemical modeler to assist with State Implementation
Plan (SIP) development and general air quality modeling assignments. The
position serves in a leadership capacity to enhance the development of the
Division’s photochemical modeling unit. The position is based in the
Division’s Central Office in Nashville, TN with potential to work remotely
based on experience.
This position requires candidates to be knowledgeable of Comprehensive
Air Quality Model with Extensions (CAMx) photochemical models and
Community Multiscale Air Quality (CMAQ), Visualization Environment for
Rich Date Interpretation (VERDI), and Sparse Matrix Operator Kernel
Emissions (SMOKE). Candidates must have significant experience in
atmospheric chemistry and physics, emission inventory development,
dispersion modeling, meteorological modeling, and/or MOVES modeling.
The position also requires a significant experience in scientific computer
programming and Geographic Information System (GIS). Preferred
candidates will be familiar with EPA rules and guidance related to emission
inventory and air quality modeling. Selected candidates will have technical
knowledge to maintain and troubleshoot Linus-based work-stations for air
quality modeling use, and use of Python and Linux coding for modeling. To
apply for this position: Careers
Highlighted Responsibilities:
Share knowledge with team unit of Comprehensive Air Quality Model
with Extensions (CAMx) photochemical models and Community
Multiscale Air Quality (CMAQ), Visualization Environment for Rich Date
Interpretation (VERDI), and Sparse Matrix Operator Kernel Emissions
(SMOKE).
Prepare data inputs for CAMx domains and emissions preprocessor
systems on a Linux domain.
Troubleshoot Linus-based work-stations for air quality modeling use and
use Python and Linux coding for modeling.
Train others in the use of Linux commands and operations for modeling.
Present work products that provide technically sound modeling results.