Bethany Goldblum, Research Engineer

Closed (1) Neutrons for National Security Applications

Applications for fall 2021 are now closed for this project.

While the global number of nuclear weapons has decreased since the mid 1980s, the number of nuclear-armed countries has continued to increase. Our group is committed to the development of neutron detection systems capable of locating special nuclear material for nuclear security applications. Neutron imaging is particularly attractive because shielding the neutron emissions of special nuclear materials is difficult and imaging capabilities can allow for source localization amid background. Scintillators have long been the primary means of detecting fission spectrum neutrons, and many new classes of materials are currently under development. Our group has been working to develop new methods for characterizing organic scintillator materials based on a modern approach.

Responsibilities of this position may include performing experimental measurements, analyzing data using a C++ software framework, Monte Carlo transport modeling using the Geant4 toolkit, and taking shifts in experimental campaigns at the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory. This apprenticeship provides opportunities for co-authorship of peer-reviewed journal articles as well as potential to transition to a paid research assistantship upon successful performance. The student is required to attend and participate in the Bay Area Neutron Group's weekly research meeting.

Day-to-day supervisor for this project: Thibault Laplace, Staff Researcher

Qualifications: Required: Lower Division Physics (7 Series) and math through Math 54; Programming fundamentals Desired skills (or what you'll learn): Upper division undergraduate standing; Proficiency in C/C++ programming; Familiarity with a Linux/Unix environment; Completion of NE101 Nuclear Reactions and Radiation (or equivalent); Experience with digital electronics and neutron detection; Proficiency in nuclear data analysis

Weekly Hours: 9-11 hrs

Off-Campus Research Site: Remote or in-person options at Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720

Related website: http://bang.berkeley.edu
Related website: http://appliedphysics.nuc.berkeley.edu/

Closed (2) NucScholar: Natural Language Processing for Nuclear Science References

Applications for fall 2021 are now closed for this project.

The goal of this work is to develop NucScholar, a new and different paradigm for the retrieval, categorization, and recommendation of nuclear physics literature. The current means by which researchers and evaluators identify and process nuclear data bibliographic information is the Nuclear Science References (NSR) database, the starting point for all nuclear structure evaluations and a platform of critical importance to the nuclear data pipeline. However, NSR is limited in capability (with a fixed set of human-derived keywords) and heavily reliant upon human intelligence tasks (e.g., bibliographic entries are generated manually by subject matter experts); thus, it is resource- and time-intensive to maintain. NucScholar provides the foundation for a sea change in NSR using a modern software framework and natural language processing tools to automatically collate and process nuclear science literature. NucScholar further expands the volume and variety of bibliographic information available to the nuclear data community without heavy reliance on human intervention.

Responsibilities of this position may include liaising with members of the U.S. Nuclear Data Program to assess needs, text mining and unstructured data extraction, NLP algorithm development, and attendance of a weekly group meeting. This assistantship provides opportunities for authorship of peer-reviewed journal articles. Successful candidates will have a passion for science and an interest in nuclear data for applications.

Day-to-day supervisor for this project: Walid Younes, Staff Researcher

Qualifications: Required: Lower Division Physics (7 Series) and math through Math 54; Programming fundamentals Desired (or what you'll learn): Upper division undergraduate standing; Proficiency in Python programming; Familiarity with a Linux/Unix environment; Completion of NE101 Nuclear Reactions and Radiation (or equivalent); Experience with methods and tools; Data visualization

Weekly Hours: 9-11 hrs

Off-Campus Research Site: Remote

Related website: https://nucleardata.berkeley.edu/
Related website: http://appliedphysics.nuc.berkeley.edu/

Closed (3) Machine Learning Applications for Nuclear Security

Applications for fall 2021 are now closed for this project.

Effective nuclear proliferation detection is hindered by the need to continuously verify the absence of undeclared nuclear materials and nuclear weapons-relevant activities. Multisensor data fusion has the potential to provide an integrated picture of difficult to detect phenomena, where composite signals can be used as proliferation indicators. Recent developments in ultra low power wireless sensor networks, in concert with advances in complexity science and statistical machine learning, offer a path forward for innovation in data analytics for proliferation detection and nuclear material security. This work uses a network of multi-sensor devices deployed at a nuclear reactor and reprocessing facility. The goal of this work is to fuse information from the many sensors and modalities to measure complex events reflective of nuclear proliferation.

The ideal candidate will have software development skills and an interest in working on concepts at the intersection of nuclear science and security policy.

The URAP student will assist in the development of machine learning algorithms in Python for pattern recognition using heterogeneous input data. The student will attend weekly research group meetings.

Day-to-day supervisor for this project: Chris Stewart, Post-Doc

Qualifications: Required: Undergraduate degree in progress; Interest in the intersection of science and nuclear security policy; Creative approach to problem-solving; Software development skills in Python Desired: Upper division undergraduate standing; Studies in mathematics, statistics, computer science, physics, or related fields

Weekly Hours: 9-11 hrs

Off-Campus Research Site: Remote or in-person options at the Nuclear Science and Security Consortium; 2150 Shattuck Ave, Suite 230; Berkeley, CA

Related website: http://complexity.berkeley.edu/
Related website: http://appliedphysics.nuc.berkeley.edu/