Academic Graduate Appointee - Cyber and Infrastructure Resilience
Academic Graduate Appointee - Cyber and Infrastructure Resilience
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1 Vacancy
Job Description
We have an opening for an Academic Graduate Appointee - Cyber and Infrastructure Resilience to work under the guidance of Cyber and Infrastructure Resilience (CIR) program staff members to conduct basic and applied research in optimization models and algorithms for infrastructure security and resilience. You will support LLNLs CIR program growing research portfolio in civil and defense critical infrastructure protection. This position is in the Optimization and Control Group of the Computational Engineering Division (CED) within the Engineering Directorate.
In this role you will
- Work under general guidance of staff engineers to contribute to research in mathematical optimization models and methods for infrastructure security assist in the implementation of specialized algorithms and participate on analysis tasks on the topics of infrastructure security hardening and resilient operation.
- Contribute to the fulfillment of research projects deliverables organizational objectives by functioning as an effective member on multi-disciplinary teams.
- Collaborate with other Lawrence Livermore National Laboratory (LLNL) scientists and engineers to accomplish research goals by bringing research results to practical use in LLNL Global Security programs.
- Offer solutions to technical problems by reviewing scientific and technical literature and experimental techniques.
- Engage other scientists frequently to share relevant knowledge opinions and recommendations.
- Contribute to scientific publications and patent applications.
- Perform other duties as assigned.
Qualifications :
- Anticipated DOE Q clearance (requires U.S. citizenship and a federal background investigation).
- Bachelors degree in Mathematics Computer Science Industrial Engineering or a related technical field.
- Experience with and demonstrated competence in one or more scientific programming languages such as Python Julia C R Matlab or Rust.
- Demonstrated ability to implement mathematical optimization problems using at least one algebraic modeling language such as Pyomo (preferred) Gurobipy JuMP AMPL GAMS among others and use standard solver packagessuch as Gurobi CBC HiGHS or Ipopt among othersto solve them.
- Effective interpersonal skills necessary to collaborate with all levels of personnel in a multi-disciplinary team environment.
- Sufficient written and verbal communication skills to prepare present explain and document technical information and publications.
Qualifications We Desire
- Familiarity with decomposition techniques in stochastic optimization such as Benders ADMM Progressive Hedging SDDP.
- Demonstrated ability to design specialized algorithmic approaches for challenging mathematical optimization problems.
Additional Information :
#LI-Hybrid
Position Information
This is a one-year Academic Graduate Appointee open to those who have been awarded a degree at the time of the employment offer.
Why Lawrence Livermore National Laboratory
- Included in 2025 Best Places to Work by Glassdoor!
- Flexible Benefits Package
- 401(k)
- Relocation Assistance
- Education Reimbursement Program
- Flexible schedules (*depending on project needs)
- Our values - visit Clearance
This position requires a Department of Energy (DOE) Q-level clearance. If you are selected we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship.
Pre-Employment Drug Test
External applicant(s) selected for this position must pass a post-offer pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.
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Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
