Apologies for cross-posting.
The Statistical Sciences group at Los Alamos National Laboratory seeks a motivated candidate for a postdoctoral research position in applied statistics and machine learning. You will work in a program that supports fundamental, interdisciplinary research to advance detection capabilities for chemical agents. Specifically, you will work with a team that is developing and building a new capability for non-invasive detection of synthetic opioids using nuclear quadrupole resonance spectroscopy.
In the twelve-month period between August 2019 and August 2020, 52,000 Americans died from synthetic opioid overdoses. Synthetic opioids, such as fentanyl, cross the United States border daily at locations such as the JFK airport. Our team is developing technology to safely and rapidly screen packages specifically for the unique chemical signatures of fentanyl and its derivatives. We aim to leverage machine learning tools to aid in signal processing and target detection.
Main focus areas of the statistical and machine learning portion of the project include i) developing tools to denoise signals in a low signal-to-noise regime that poses challenges to existing signal processing techniques and ii) developing and testing cutting-edge deep learning-based signal detection and matching capabilities against standard techniques. Not only will this project offer publishing opportunities in statistics and machine learning methodology and applications, but it also offers the opportunity to contribute directly to a deployable detection capability for stemming the flow of synthetic opioids into the United States. Machine learning and statistics tools that you develop will likely benefit other detection modalities and applications, further increasing the real-world impact of your work.
Link to job posting with more information------------------------------
Natalie Klein, P.h.D.
Statistical Sciences Group
Los Alamos National Laboratory
Los Alamos, NM
neklein@lanl.gov------------------------------