What are the responsibilities and job description for the Ocean Model Data Assimilation AI/ML Researcher position at Peraton?
About Peraton
Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world's leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace. The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees do the can't be done by solving the most daunting challenges facing our customers. Visit peraton.com to learn how we're keeping people around the world safe and secure.
Program Overview
About The Role
Peraton is seeking a Ocean Model Data Assimilation AI/ML Researcher at Stennis Space Center in MS. The geographic scope of the battlespace environment is expanding, placing new demands on existing modeling efforts in dynamic regions of the global ocean and near the seafloor. To meet operational modeling needs in these regions, the Navy Research Laboratory (NRL) is developing methods to refine the model grid for HYCOM, NCOM, Wavewatch III and other models using cutting-edge artificial intelligence and machine learning (AIML). Methods for automatically identifying regions with oceanographic features of interest such as fronts, Langmuir cells, eddies, convection, stratification, etc. in model output must be developed and successful grid refinement anticipated to improve resolution of tactically relevant features that would otherwise be overlooked.
Since AIML techniques can be computationally expensive but cannot serve as a replacement for numerical modeling and the numerical models perform well for most locations in time and space, this project will use a separate model trained on historical model output to detect regions of interest. Once trained, calling the model to identify regions of interest will be relatively inexpensive. Within these regions, the grid will be refined to improve resolution of local dynamical features. Since training AIML models can require substantial amounts of data, and observations in the ocean are typically sparse, historical model data will be used to inform the optimal grid adjustments.
Responsibilities will include:
PhD in Mathematics or an Applied Science Field
Details
Target Salary Range: $66,000 - $106,000. This represents the typical salary range for this position based on experience and other factors.
EEO:Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law.
Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world's leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace. The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees do the can't be done by solving the most daunting challenges facing our customers. Visit peraton.com to learn how we're keeping people around the world safe and secure.
Program Overview
About The Role
Peraton is seeking a Ocean Model Data Assimilation AI/ML Researcher at Stennis Space Center in MS. The geographic scope of the battlespace environment is expanding, placing new demands on existing modeling efforts in dynamic regions of the global ocean and near the seafloor. To meet operational modeling needs in these regions, the Navy Research Laboratory (NRL) is developing methods to refine the model grid for HYCOM, NCOM, Wavewatch III and other models using cutting-edge artificial intelligence and machine learning (AIML). Methods for automatically identifying regions with oceanographic features of interest such as fronts, Langmuir cells, eddies, convection, stratification, etc. in model output must be developed and successful grid refinement anticipated to improve resolution of tactically relevant features that would otherwise be overlooked.
Since AIML techniques can be computationally expensive but cannot serve as a replacement for numerical modeling and the numerical models perform well for most locations in time and space, this project will use a separate model trained on historical model output to detect regions of interest. Once trained, calling the model to identify regions of interest will be relatively inexpensive. Within these regions, the grid will be refined to improve resolution of local dynamical features. Since training AIML models can require substantial amounts of data, and observations in the ocean are typically sparse, historical model data will be used to inform the optimal grid adjustments.
Responsibilities will include:
- Develop a lightweight algorithm for detecting regions that demand a particular model resolution due to underlying physics.
- Develop a grid-refinement model to adjust existing numerical models locally in real time.
- Perform validation for challenging physical processes in observed regions of Navy interest.
- Evaluate results by determining success rate for detection of regions of interest, numerical stability of grid refinement, and faithful resolution of observed physical processes.
PhD in Mathematics or an Applied Science Field
- Must have coding experience in MATLAB, Python, and C .
- Must have experience with Unix/Linux, Shell Scripting, and High Performance Computing (HPC).
- Must have experience working with standard U.S. Navy ocean models such as HYCOM, NCOM, and Wavewatch III and ocean model reanalysis..
- Must have thorough knowledge of oceanography and mesoscale ocean features such as eddies, fronts, Langmuir cells, and eddy shedding events within ocean currents.
- Experience working in a Department of Defense research facility strongly preferred.
- Ability to obtain a Secret Clearance
Details
Target Salary Range: $66,000 - $106,000. This represents the typical salary range for this position based on experience and other factors.
EEO:Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law.
Salary : $66,000 - $106,000
