AI/Machine Learning Research Scientist – Undersea Acoustics & Physics-Based Modeling
Peraton · Stennis Space Center, MS, US
About this role
## Overview Peraton is seeking an **AI/Machine Learning Research Scientist** to work **onsite at the Stennis Space Center**. You will conduct **basic and applied research** in **machine learning, deep learning, generative AI, large language models, and physics-based machine learning**—with a primary focus on **underwater acoustics and acoustic propagation**. ## Responsibilities - Collaborate with **acousticians, physicists, engineers, materials scientists, computer scientists**, and other subject-matter experts to develop and improve ML approaches for complex technical problems. - Conduct research in **deep learning, generative AI, large language models, and scientific machine learning** for acoustic and physics-based applications. - Develop, evaluate, and implement: - **Physics-informed neural networks** - **Neural operators** - **Surrogate models** - **Hybrid physics–machine-learning approaches** - **Generative models** - Design and execute controlled experiments to **train, test, and evaluate** ML models. - Assess **accuracy, robustness, reliability, physics consistency, and generalization** to conditions not represented in training data. - Build ML surrogates and other accelerated computational approaches to reduce the cost of full-physics simulations while maintaining **decision-relevant fidelity**. - Translate physics and engineering requirements into practical ML approaches, including **data representations, performance metrics, and validation protocols**. - Develop and maintain **clear, reproducible research software**, technical documentation, and experimental records. - Analyze quantitative results, identify **limitations, uncertainty, and reliability considerations**, and communicate findings via **reports, publications, briefings, and presentations**. ## Qualifications **Required** - **8+ years** with BS/BA; **6+ years** with MS/MA; **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 ML/deep learning to **scientific, engineering, physics-based, or computational modeling**. - Experience developing/training/testing/evaluating ML models using modern frameworks such as **PyTorch, TensorFlow, or JAX**. - Knowledge of core ML concepts: optimization, regularization, representation learning, generative modeling, data management, evaluation, and reproducible experimentation. - Ability to formulate research problems, design controlled experiments, analyze results, identify limitations, and communicate defensible conclusions. - Ability to work independently while collaborating with multidisciplinary teams and operational stakeholders. - Ability to develop and maintain reproducible research software and documentation. **Desired** - **Active Secret clearance** or higher. - Experience with **underwater acoustics, acoustic propagation, wave physics, signal processing**, or related computational-physics applications. - Experience with **physics-informed machine learning** (PINNs, neural operators, surrogate/reduced-order modeling, hybrid approaches). - Experience developing surrogate models/emulators for computationally intensive simulations. - Familiarity with **uncertainty estimation**, reliability assessment, and evaluation under **distribution shift**. - Experience applying **generative AI/LLMs** to scientific/engineering/decision-sup
Listing freshness
CronJobs last confirmed this listing 2h ago. If its source stops confirming the opening for seven days, this page is removed from active inventory.