Skip to main content

Junior/Assistant/Associate Specialist - Nuclear Engineering - College of Engineering

Recruitment Period

Open date: November 1st, 2019
Next review date: Monday, Jan 6, 2020 at 11:59pm (Pacific Time)
Apply by this date to ensure full consideration by the committee.
Final date: Monday, Jan 6, 2020 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.


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.

Appointment length:
This is a full-time, nine month position. The appointment may be renewable on an ongoing basis depending on availability of funding and satisfactory performance. Anticipated start date is Spring 2020.

Working within a team of nuclear engineerings and computer scientists, the candidate will primarily contribute through software development and data analysis. Additional tasks may include supporting experimental data collection campaigns at nuclear reactor facilities. This position is under the supervision of Dr. Bethany Goldblum.

Basic Qualifications (at the time of application):
Bachelor’s degree or equivalent international degree

Preferred Qualifications:

  • Bachelor's degree in Computer Science, Physics, Nuclear Engineering, or other closely related discipline
  • Software development experience and strong programming skills
  • Familiarity with a Linux/Unix environment
  • Experience with machine learning and efficient algorithms
  • Familiarity with discrete mathematics and probability theory
  • Demonstrated creativity and highly developed problem solving skills
  • Excellent verbal and written communication skills and teamwork

The full-time annual salary will be commensurate with qualifications and experience.

Application Procedure:
To apply go to
Please direct all questions for this position to Bethany Goldblum,, with the subject line: Complexity Science Jr/Assistant/Associate Specialist.

This position will remain open until filled.

3 References required (contact information only): Letters of reference may be solicited for finalists. We will seek your permission before contacting your references.

All letters will be treated as confidential per University of California Policy and California State law. Please refer potential referees, including when letters are provided via a third party (i.e., dossier service or career center), to the UC Berkeley statement of confidentiality: ( prior to submitting their letters.

The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status. For the complete University of California nondiscrimination and affirmative action policy see:

Job location

Berkeley, CA

Learn More

More information about this recruitment:


Document requirements
  • Curriculum Vitae - Your most recently updated C.V.

  • Cover Letter

Reference requirements
  • 3 required (contact information only)