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The Complexity Science research team in the Department of Nuclear Engineering at the University of California, Berkeley welcomes applications for a Junior/Assistant/Associate Specialist in machine learning and algorithm development. The goal of this work, in collaboration with Lawrence Berkeley National Laboratory, is to develop and apply transferable multi-source machine learning methods to classify nuclear operations—such as reactor operational states, fuel delivery, and reactor refueling—at previously unseen facilities. Transferability, the application of ML models generated at one facility to other contexts or settings, is critical to the application of multi-modal informatics in proliferation detection scenarios. This work involves the implementation and evaluation of transductive transfer learning methods including for example semi-supervised learning under clustering/manifold assumptions and weak supervision using labeling functions derived from domain expertise.

Responsibilities:
Working within a team of nuclear engineering and computer scientists, the candidate will primarily contribute through software development and data analysis. Job duties include developing algorithms to clean and preprocess data; programming and evaluating machine learning methods for transferability; data visualization and interpretation; preparation of written reports and oral presentations; as well as opportunities for the development of peer-reviewed publications. This position is under the supervision of Dr. Bethany Goldblum.

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