Polar and Machine Learning Meteorology Research Scientist

SAIC

6h•Remote

About The Position

SAIC is seeking a Polar and Machine Learning Meteorology Research Scientist to support the US Naval Research Laboratory (NRL) Marine Meteorology Division(MMD) in Monterey, CA. This position is 100%25 Remote. The Polar and Machine Learning Meteorology Research Scientist will be responsible for but not limited to the following: Conduct Research on ML Weather Models: Conduct assessments of the ML models to identify its specific strengths, weaknesses, and suitability for key Navy requirements Perform and analyze performance benchmarks by comparing ML forecasts against the global models using standard verification metrics Assess the potential for ML models to drive downstream models Implement and utilize AI-accelerated sensitivity analysis to efficiently identify the most impactful locations for new meteorological observations, particularly in data-limited environments Investigate the fundamental characteristics of error growth and predictability limits in ML weather models, comparing them against traditional physics-based numerical weather prediction models Design and execute targeted perturbation experiments to test scientific hypotheses related to ML model error dynamics and their ability to capture high-impact, extreme weather events Investigate and Model Arctic Weather Phenomena: Analyze the formation, dynamics, and predictability of polar lows, identifying key processes that contribute to these hazardous weather systems Support Operational Transition and Programmatic Requirements: Develop and define transition pathways for implementing validated ML models and tools into operational use at the Fleet Numerical Meteorology and Oceanography Center (FNMOC) Provide expert technical support to FNMOC for the adoption and maintenance of transitioned ML-based forecasting systems Prepare and deliver written materials, technical reports, and summary figures for program reviews and official reports

Requirements

Bachelor's degree and three (3) years’ experience; additional four (4) years’ experience can be considered in lieu of degree
Must possess an active Secret security clearance with the ability to maintain; US Citizenship required
Demonstrated experience in: Machine Learning related to NWP modeling and applications related to US Navy operations
Key processes and prediction of Polar Meteorology
Transition of NWP models to Navy operational METOC Centers
Must have demonstrated report writing experience
Must have demonstrable data processing experience

Nice To Haves

Experience with a combination of FORTRAN, Cylc, MATLAB, Python, or Shell Scripting programming languages
Experience publishing peer-reviewed scientific literature, software documentation, guidebooks, and/or handbooks
High Performance Computing (HPC) experience

Responsibilities

Conduct assessments of the ML models to identify its specific strengths, weaknesses, and suitability for key Navy requirements
Perform and analyze performance benchmarks by comparing ML forecasts against the global models using standard verification metrics
Assess the potential for ML models to drive downstream models
Implement and utilize AI-accelerated sensitivity analysis to efficiently identify the most impactful locations for new meteorological observations, particularly in data-limited environments
Investigate the fundamental characteristics of error growth and predictability limits in ML weather models, comparing them against traditional physics-based numerical weather prediction models
Design and execute targeted perturbation experiments to test scientific hypotheses related to ML model error dynamics and their ability to capture high-impact, extreme weather events
Analyze the formation, dynamics, and predictability of polar lows, identifying key processes that contribute to these hazardous weather systems
Develop and define transition pathways for implementing validated ML models and tools into operational use at the Fleet Numerical Meteorology and Oceanography Center (FNMOC)
Provide expert technical support to FNMOC for the adoption and maintenance of transitioned ML-based forecasting systems
Prepare and deliver written materials, technical reports, and summary figures for program reviews and official reports