Post-Master's Research Associate - Signal Processing & Machine Learning - Disruptive Technology

Pacific Northwest National Laboratory • Richland, WA

1d

About The Position

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 Physical Detection Systems and Deployment Division, part of the National Security Directorate, delivers policy-informed technology solutions by removing barriers to real-world implementation. We strive to understand end-user environments to transition technology from the developmental stage to deployment. Our diverse expertise in operational systems provides tools, technologies, and approaches for combating a range of threats, both at home and in more than 100 countries around the globe.
The Disruptive Technology group is building practical AI-enabled sensing capability that can accelerate mission delivery across applied national security projects.
This role is aimed at a recent master's graduate who can contribute immediately to advanced signal-processing and machine-learning problems using real sensor data.
The associate will support multiple projects and should be ready to move between model development, quantitative evaluation, and technical collaboration with hardware teams.
The associate will work as a technical contributor under the guidance of scientists and engineers, and will be responsible for performing the following technical tasks:

Requirements

• Candidates must have received a Master’s degree within the past 24 months or within the next 8 months from an accredited college or university.

Nice To Haves

• Master's degree in Electrical Engineering, Computer Science, Applied Mathematics, Physics, or a related field completed by start date.
• Candidate should demonstrate at least two of the following: machine learning for spatial or time-series data, complex-valued machine learning, classical signal processing (DSP).
• Strong Python-based technical computing experience; PyTorch or similar frameworks preferred.
• Ability to translate ambiguous technical goals into executable analyses.
• Experience with sensor data, RF/electromagnetic systems, or applied research environments.

Responsibilities

• Develop and evaluate machine-learning methods for spatial or time-series data.
• Apply classical signal-processing techniques and modern ML methods to sensing problems.
• Work with complex-valued data representations when appropriate to the mission need.
• Build reproducible analysis code, experiments, and visualizations in Python-based workflows.
• Communicate results clearly and collaborate with domain experts, including hardware-focused staff.

Benefits

• Employees and their families are offered medical insurance, dental insurance, vision insurance, robust telehealth care options, several mental health benefits, free wellness coaching, health savings account, flexible spending accounts, basic life insurance, disability insurance, employee assistance program, business travel insurance, tuition assistance, relocation, backup childcare, legal benefits, supplemental parental bonding leave, surrogacy and adoption assistance, and fertility support.
• Employees are automatically enrolled in our company-funded pension plan and may enroll in our 401 (k) savings plan with company match.
• Employees may accrue up to 120 vacation hours per year and may receive ten paid holidays per year.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Number of Employees: 1,001-5,000 employees