Data Scientist II - Machine Learning Undersea Acoustics

At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget.

Our Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus.

The National Security Directorate (NSD) drives science-based, mission-focused solutions to take on complex,real-world threats to our nation and the world.

The AI and Data Analytics Division, part of the National Security Directorate, combines profound domain expertise and creative integration of advanced hardware and software to deliver computational solutions that address complex data and analytic challenges. Working in multidisciplinary teams, we connect foundational research to engineering to operations, providing the tools to innovate quickly and field results faster. Our strengths are integrated across the data analytics lifecycle, from data acquisition and management to analysis and decision support.

PNNL is seeking a Data Scientist II to investigate and develop deep learning methods for undersea acoustics. You will analyze acoustic data, formulate and test algorithms, and develop, train, and evaluate deep learning models for detecting, classifying, localizing, or characterizing underwater sources and phenomena. This research supports national security missions and may address challenges relevant to naval sensing and undersea operations.

In the AI and Data Analytics Division, you will independently carry out defined research tasks as part of a team developing analytical methods and tools that support acoustic analysis and national missions.

Current priorities include applying deep learning to acoustic data and using Fourier analysis or signal processing to develop and evaluate methods for detecting, classifying, or characterizing underwater sources. The role also offers opportunities to explore related problems in time-series analysis, representation learning, anomaly detection, uncertainty quantification, and multimodal sensing. Candidates with deep learning experience in undersea acoustics, sonar, audio, radar, seismic, or related sensing applications are encouraged to apply.

The role may also include prototyping and evaluating agentic SI systems that combine deep learning with acoustic analysis tools.

You will work alongside researchers and domain specialists on difficult national science and technology challenges. Collaboration with software and machine learning (ML) engineers will reflect each project's needs and stage of development, including transition of tested methods into usable tools. Opportunities to advance existing research and pursue new ideas may come through ongoing projects, internal research investments, or new proposals.

• Develop, train, and evaluate deep learning methods for undersea acoustic detection, classification, localization, separation, and related problems.
• Formulate and investigate research questions through Fourier analysis, signal processing, mathematical analysis, algorithm development, and experiments.
• Design reproducible experiments and compare methods, examining detection and classification performance, false alarms, robustness to propagation and environmental variability, uncertainty, and computational demands.
• Analyze and interpret results, document methods and limitations, and communicate findings to project leads, collaborators, and mission partners.
• Prepare…