2016 LANL Internship Opportunities

2016 LANL Internship Opportunities


Nestled in the mountains of beautiful northern New Mexico, Los Alamos National Laboratory (LANL) is dedicated to solving national security challenges through scientific excellence.  Learn more about the lab’s mission and career opportunities here: http://www.lanl.gov/. The following projects represent a sample of 2016 internship opportunities at LANL, but many more projects are available. If you are a CVT student interested in interning at LANL but don’t see a project listed that aligns with your research goals, please contact Karen Miller (kamiller@lanl.gov) for more information.

Title: Next Generation Safeguards Initiative Spent Fuel Project

POC: Alexis Trahan (NEN-1, atrahan@lanl.gov)

The purpose of the Next Generation Safeguards Initiative (NGSI)–Spent Fuel (SF) project is to strengthen the technical toolkit of safeguards inspectors and/or other interested parties. The Differential Die-Away (DDA) and Differential Die-Away Self-Interrogation (DDSI) instruments are nondestructive assay techniques that have been investigated through the NGSI-SF project, and the latter has been built and undergone preliminary testing at Los Alamos National Laboratory. Additional work will be done with MCNP to determine fuel parameters using the DDSI signal and to prepare the DDSI instrument for spent fuel measurements at the end of 2016. The DDA instrument is scheduled to be built in the spring of 2016 and will undergo testing in the summer of 2016. Fresh fuel assemblies will be measured and characterization measurements will be performed. The final objective of this project is to quantify the capability of several integrated NDA instruments to use combined signatures of neutrons and gamma rays. The Safeguards Science and Technology Group is looking for one graduate level summer intern to research within these projects in the summer of 2016. The student should have experience with MCNP and will perform simulations as well as hands-on laboratory experimental work with the DDA instrument. A peer-reviewed journal publication is the expected output of this work.


Title: Opportunities in the Advanced Nuclear Technology Group

POC: Jeff Goettee (NEN-2, goettee@lanl.gov)

The Advanced Nuclear Technology group (NEN-2) at Los Alamos conducts research, development, training, and operations in the application of passive and active nuclear measurement techniques as well as radiography. Applications of primary interest to the group include weapons of mass destruction, nuclear emergency response programs, intelligence support activities, and international treaty verification. NEN-2 also conducts nuclear criticality research supporting basic research in nuclear chain-reacting systems and contributing to arms control and treaty verification, waste assay, safeguards and accountability, and environmental restoration. A key component of the NEN-2 mission is to teach a wide variety of personnel how to safely handle, detect, characterize, and manage nuclear materials. NEN-2 staff members conduct classes in criticality safety, nuclear materials handling methods, and nuclear instrumentation.


Title: Opportunities in the Statistical Sciences Group

POC: Jim Gattiker (CCS-6, gatt@lanl.gov)

The Los Alamos Statistical Science group is a capability at the laboratory in statistical methods, applications, and foundations. We are interested in students who would like to develop their education in statistics by applying their skills in a collaborative R&D environment. Potential topics areas include:

  • Methods development in statistical modeling and statistical computation,
  • Applied statistical analysis of experimental data and computer experiments in physics and engineering, for example, computational fluid dynamics, additive manufacturing, carbon capture, nuclear materials analysis, etc.
  • Systems reliability and failure analysis
  • Information networks such as cyber-physical systems, multi-source analysis and attribution.


Title: Uncertainty & Data Analysis Applications

POC: Christy Ruggiero, Kari Sentz, Diane Oyen (NEN-5 & ISR-3; ruggiero@lanl.gov)

We are interested in students with computer science, math, or statistics backgrounds for a variety of problems in data analysis and uncertainty. Current projects include the following: Quantitative analysis of nuclear material in images (microscopy, but also other image types) and defining uncertainty in material analysis for forensics applications; image segmentation and object recognition in nuclear material images and other “nuclear” image data sets; and applying machine learning and image analysis tools to problems in safeguards. 


Title: Developing Accurate MCNP6 Simulations of Correlated Data in Fission Events

POC: Michael Rising (XCP-3, mrising@lanl.gov)

With new physics models describing the event-by-event simulation of fission reactions recently integrated into the MCNP6 general purpose radiation transport code, there is a need to study and benchmark the code predictions against experimental data. The entire scope of this project ranges from understanding the nuclear physics simulations of secondary particle emissions from fission, to modeling and simulating both differential and integral experiments using the new MCNP6 features, and finally comparing the code predictions to real-world measurements of the signatures of special nuclear materials (SNM). The skills necessary to develop and assess the predictive capability of these new MCNP6 features include:

  • Experience with programming languages such as C, C++, and Fortran
  • Experience with scripting languages such as Perl, Python, etc.
  • Experience using MCNP with some knowledge of transport theory
  • Excellent writing and communication skills
  • Ability to work effectively in a team

The student(s) will be encouraged to publish their work in journals and /or conference proceedings and to orally present their work at an appropriate venue.


Title: Inverse Transport Methods for Reconstructing the Isotopic Composition of a Shielded Source

POC: Jeffrey Favorite (XCP-3; fave@lanl.gov)

One of the unknown features in a radioactive source/shield system that we hope to reconstruct using inverse transport methods is the isotopic composition of the radiation source—for example, the 235U enrichment. Isotopic weight fractions for a material must satisfy a normalization constraint. This project will address the question of how best to impose the normalization constraint for derivative-based as well as derivative-free optimization schemes. The student should be strong in mathematics. The student will modify an existing FORTRAN code and so should have some familiarity with FORTRAN and programming but need not be an expert. The student will learn about computational transport theory but need not be an expert. Success on this project will lead to a conference paper and/or a journal article.