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Job Location (Full Address):
120 Trustee Rd, Rochester, New York, United States of America, 14627
Opening:
Worker Subtype:
Temporary
Time Type:
Full time
Scheduled Weekly Hours:
40
Department:
210560 Earth & Environmental Sciences
Work Shift:
UR - Day (United States of America)
Range:
UR URG 102 H
Compensation Range:
$17.00 - $22.10
The referenced pay range represents the minimum and maximum compensation for this job. Individual annual salaries/hourly rates will be set within the job's compensation range, and will be determined by considering factors including, but not limited to, market data, education, experience, qualifications, expertise of the individual, and internal equity considerations.
Responsibilities:
Performs routine and standardized computational research activities in support of atmospheric science investigations. Assists with the setup, operation, and application of established modeling tools and data assimilation frameworks. Prepares datasets, executes prescribed model runs, processes results, and records outputs in accordance with standard practices and procedures. Supports the development, testing, and refinement of methods under guidance.
ESSENTIAL FUNCTIONS
- Assists with the setup and execution of standardized computational analyses and prescribed model runs using existing atmospheric chemistry and transport modeling frameworks.
- Prepares and organizes observational and model input datasets following established protocols.
- Extracts data from designated sources and processes datasets using well-defined, documented methods. Assists with generating routine figures, tables, and summary outputs.
- Performs routine quality control, file management, and documentation of computational procedures and results.
- Other duties as assigned.
MINIMUM EDUCATION & EXPERIENCE
- Bachelor’s degree required
- Experience with using programming languages (R, python, and/or Fortran) for computation and visualization required
KNOWLEDGE, SKILLS AND ABILITIES
- Experience with chemical transport modeling, inverse methods (4D-VAR/adjoint), and handling large observational datasets preferred
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