Postdoctoral Appointee- Data Science Energy Density , Onsite

Job

Location:

Albuquerque, NM

Employment Type:

Full-time, Temporary

The Radiation and Fusion Target Design department has an opening for a postdoctoral researcher to advance the use of data science and machine learning in the design and understanding of experiments on the Z facility. A particular emphasis will be on applying these methods to the emerging design space for layered MagLIF targets under tailored pulse shapes. Most Z fusion experiments utilize a synchronous short pulse waveform to provide maximum power to the target and minimize time for instability growth.

However, the recent proposal of multi‑layer targets utilizing heterogeneous materials opens the design space to improve performance by considering differences in material response. The successful applicant will use data science techniques to leverage the large existing set of data developed at the Z machine and collaborate with other members of the pulsed power sciences center to explore this design space and propose novel fusion experiments for the Z machine.

• Partner on the design and analysis of experimental campaigns at Z
• Leverage advances in machine learning to enable more rapid design iteration for Z experiments
• Represent the research and development in the organization through publications in scientific journals and presentation at conferences

• Collaborate as part of an integrated team to plan research and improve the utilization of data science and machine learning techniques at the Z facility
• Develop data pipelines, automated analysis methods, and machine‑learning tools to improve the interpretation and design of Z experiments
• Publish findings and interact with the academic community

• Ph.D. in Physics, Applied Physics, Electrical Engineering, Computer Science, Astronomy, or a related discipline (Ph.D. must be conferred within five (5) years prior to employment)
• Ability to obtain and maintain a DOE Q security clearance

• Experience in the curation and utilization of scientific data to accelerate research
• A technical competency in Bayesian or other relevant optimization methodologies
• Excellent communication skills demonstrated at technical conferences

Applicants must be able to obtain and maintain a DOE Q‑level security clearance. This requires U.S. citizenship and may be impacted if you hold dual citizenship.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status and any other protected class under state or federal law.