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.
Responsibilities
Peraton is seeking an AI/Machine Learning Research Scientist to work onsite at the Stennis Space Center to conduct basic and applied research in machine learning, deep learning, generative artificial intelligence (AI), large language models, and physics-based machine learning. This role will develop and evaluate advanced computational methods for Navy-relevant scientific, engineering, acoustic, and decision-support challenges.
The successful candidate will focus primarily on underwater acoustics and acoustic propagation, with potential applications in wave physics, signal processing, and related computational-physics domains. The position will develop rapid machine-learning surrogate models for computationally demanding physics models while preserving decision-relevant fidelity, improving computational efficiency, and supporting reliable technical analysis.
What You Will Do
Collaborate with acousticians, physicists, engineers, materials scientists, computer scientists, and other subject-matter experts to develop and improve machine-learning approaches for complex technical problems
Conduct research in deep learning, generative AI, large language models, and scientific machine learning to support acoustic and physics-based applications
Develop, evaluate, and implement physics-informed neural networks, neural operators, surrogate models, hybrid physics-machine-learning approaches, and generative models
Design and execute controlled experiments to train, test, and evaluate machine-learning models
Assess model accuracy, robustness, reliability, physics consistency, and generalization to conditions not represented in training data.
Develop machine-learning surrogates and other accelerated computational approaches that reduce the cost of full-physics simulations while maintaining fidelity to decision-relevant outputs
Translate physics and engineering requirements into practical machine-learning approaches, data representations, performance metrics, and validation protocols
Develop and maintain clear, reproducible research software, technical documentation, and experimental records
Analyze quantitative results; identify model limitations, uncertainty, and reliability considerations; and communicate technically sound conclusions through reports, publications, briefings, and presentations
Qualifications
What you will need:
Minimum of 8 years with BS/BA; Minimum of 6 years with MS/MA; Minimum of 3 years with PhD
Ability to obtain and maintain a Secret security clearance
U.S. citizenship
Strong analytical, problem-solving, technical communication, and quantitative research skills
Demonstrated experience applying machine-learning or deep-learning methods to scientific, engineering, physics-based, or computational modeling problems
Experience developing, training, testing, and evaluating machine-learning models using a modern framework such as PyTorch, TensorFlow, or JAX
Knowledge of core machine-learning concepts, including neural-network optimization, regularization, representation learning, generative modeling, data management, model evaluation, and reproducible experimentation
Ability to formulate research…