Acoustic Signal Processing Machine Learning Graduate Student

Los Alamos National Laboratory's Applied Acoustics Team (MPA-11) is seeking a Graduate Student with strong computational skills and a solid background in signal processing and machine learning to contribute to noninvasive acoustic/ultrasonic characterization and monitoring in complex, noisy systems. The role involves automating the recording, conditioning, and processing of real-world measurements in the time- and frequency-domains, and developing machine learning algorithms to extract information from signals.

Some experimental acoustic measurement technique development may also be involved.

• Contribute to development of new sensing technologies and techniques for material characterization (solids, liquids, gases), acoustical imaging, nonlinear acoustics, and monitoring of structures over time.
• Automate recording, conditioning, and processing of real-world measurements in time and frequency domains.
• Develop and apply machine learning algorithms to extract useful information from acoustic signals.
• Participate in experimental technique development and instrumentation customization as needed.
• Collaborate within a multidisciplinary team; publish results and present at sponsors’ meetings and conferences; contribute to patents when appropriate.
• Explore application areas including corrosion detection and structural health monitoring.

• Minimum required:
  Graduate Student in Applied Physics, Engineering, Computer Science, or a closely related field; degree earned within the last five years or expected soon.
• Strong technical background in one or more of: signal processing, advanced data analysis, statistics, ultrasonic techniques, nondestructive testing, and machine learning.
• Fluency in MATLAB, Python, or C++.
• Experience with standard ML software packages (e.g., PyTorch, Scikit-learn).
• Experience with deep learning architectures such as MLPs, CNNs, and transformers.
• Experience developing and validating ML methods for signal-processing tasks using complex, noisy real-world data.
• Track record of original scientific research through peer-reviewed publications or conference presentations.

• Knowledge of electronics and common lab instruments (function generators, oscilloscopes, piezoelectric transducers).
• Experience with generative models (GANs, CVAEs, etc.).
• Experience with edge learning on hardware such as FPGAs.
• Adaptability and willingness to learn new areas of research as needed.
• Ability to obtain DOE Q clearance (U.S. citizenship often required).

Work Location:

onsite in Los Alamos, NM. All work locations are at management’s discretion.

Contact:
Dr. John Greenhall, jgreenhall

This position requires the ability to obtain a DOE Q clearance. U.S. citizenship is typically required; foreign nationals may require additional authorization. DOE background investigations apply. See DOE orders for details and eligibility.

Located in northern New Mexico, LANL is a multidisciplinary research institution engaged in strategic science for national security. We offer a comprehensive benefits package including:

• PPO or high-deductible medical insurance with nationwide network
• Dental and vision insurance
• Free basic life and disability insurance
• Paid maternity and parental leave
• 401(k) with matching and annual contributions
• Learning opportunities and tuition assistance
• Flexible schedules and paid time off
• Onsite gyms and wellness programs
• Relocation assistance

Follow LANL's application steps. If you have an existing LANL job account or have been insured by LANL previously, activate your account and apply as directed. If you are new to LANL, register to create an account and apply.

• Seniority level:
  Not Applicable
• Employment type:
  
  Full-time
• Job function:
  Other
• Industries:
  Defense and Space;
  Manufacturing and Research Services

LANL is an equal opportunity employer. All employment practices are based on qualification and merit, without regard to protected categories. The Laboratory is committed to accessibility and reasonable accommodations; to request accommodations, contact applyhelp or call  opt. 3.