The Technical Reachback Project is seeking a graduate student intern who is interested in using predictive modeling and/or machine learning to make predictions. The intern will be working at our California campus during the summer of 2017.
The Department of Homeland Security (DHS) Domestic Nuclear Detection Office (DNDO) is implementing a domestic system to detect attempts to import or transport radiological or nuclear material intended for illicit use. Technical Reachback uses data, collected over many years from radiation monitors on highways, airports, and seaports, to develop products that enhance the ability to recognize anomalous radiation profiles in trucks, cars, and shipping containers. Technical Reachback draws on the expertise of radiation physicists at Los Alamos and Sandia National Laboratories. Sandia National Laboratories has developed software to assist in analyzing existing data from radiation monitors to develop "signatures" that model threat and non-threat situations. These signatures provide federal and state officials with an additional method for determining whether a vehicle with an anomalous radiation profile is a threat.
The Technical Reachback Project has a vast collection of radiation readings. The project would like to make predictions on the spectral response of detectors at different geographical locations and under different environmental conditions. The project is looking for a predictive big data analytics software developer to develop a machine learning model.