SAIC has an opportunity for a Research Scientist to provide technical support
services in applying Machine Learning to Navy Numerical Weather Prediction (NWP)
development, and testing. Work shall be performed in accordance with all
applicable DSRC usage policies, security requirements, and Government-furnished
technical direction.
This position is a remote position.
Task 1: High-Resolution Machine Learning Weather Model Testing and NWP
Reanalysis: Evaluate emerging high-resolution AI weather prediction systems
(such as Atmo.ai) for Navy-relevant maritime environments, and develop a
high-resolution, multi-year COAMPS-based regional weather reanalysis dataset
over the Western Pacific to support model evaluation and scientific study.
Task 2: Navy Machine Learning Global Weather Model (Validation and Transition
Effort): Fine-tune and transition a GraphCast-based global machine learning
weather model into FNMOC operations, evaluate additional emerging ML weather
models, and coordinate the development of a lightweight regional ML model to
improve maritime decision-making and forecasting.
Task 3: Multiscale Predictability of Atmospheric Rivers and Air-Sea
Interaction: Investigate the fundamental physical processes, air-sea fluxes, and
boundary layer influences that affect the multiscale predictability of
atmospheric rivers using advanced Navy prediction systems, machine learning
models, and targeted field observations (e.g., dropsondes and buoys) to improve
high-impact weather forecasting operational suitability of new observation
sources.
QUALIFICATIONS
EDUCATION AND EXPERIENCE:
Bachelors and two (2) years or more experience; Masters and 0 years related
experience; PhD
PhD in Atmospheric Sciences or a related scientific or engineering field of
study
Shall have at a minimum of 3 years of demonstrated research experience in:
Applying machine learning models to probabilistic weather forecasting
Evaluating forecast skill and systematic biases in NWP reforecasts
Improving decision-making under Identified regime-dependent uncertainty
Evaluating AI climate emulators to reproduce and forecast
stratosphere-troposphere coupling
Programming proficiency in Python, R, and SQL
Must have experience with a combination of AWS, Git/GitHub, High-Performance
Computing (HPC), GPU-accelerated computing, ERA5/ERA-I, ECMWF and UFS
reforecast, AI-based climate/weather emulators (GraphCast, ACE2).
Desired Experience:
Experience publishing peer-reviewed scientific literature, software
documentation, guidebooks, and/or handbooks.
High Performance Computing (HPC) experience.
CLEARANCE REQUIREMENT:
Candidate required a completed T3 Security Investigation for full access to
government IT systems
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