Numerical Optimization and Advanced Machine Learning Methods - Postdoctoral Researcher

We have an opening for a Postdoctoral Researcher to contribute to fundamental R&D in optimization under uncertainty and machine learning in support of projects related to the optimization of energy systems using high-performance computing and uncertainty quantification for deep learning models. These interdisciplinary projects aim to combine state-of-the-art numerical algorithms for continuous optimization with machine learning models to reach various scientific objectives. You will develop numerical optimization algorithms and machine learning surrogates for scalable dimensionality reduction and uncertainty quantification of large-scale optimization problems related to optimal design and control of complex energy systems. This position will be in the Uncertainty Quantification and Optimization Group in the Center for Applied Scientific Computing (CASC) Division within the LLNL Computing Principal Directorate.

Essential Duties

• Research, design, implement, and apply numerical optimization and advanced machine learning methods for multiple applications in a collaborative scientific environment.
• Actively participate with project scientists and engineers in defining, planning, and formulating experimental, modeling, and simulation efforts for complex energy systems.
• Propose and implement advanced analysis methodologies, collect and analyze data, and document results in technical reports and peer-reviewed publications.
• Contribute to grant proposals and collaborate with others in a multidisciplinary team environment, including academic and industrial partners, to accomplish research goals.
• Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internal and external to the Laboratory.
• Perform other duties as assigned.

• Ph.D. in Optimization, Machine Learning, Operations Research, Computer Science, Applied Mathematics, Electrical Engineering, or a related field.
• Demonstrated research productivity, as documented by peer-reviewed publications, reports, presentations, and/or open-source software.
• Strong theoretical background in mathematical optimization and related topics.
• Experience in developing, implementing, and applying advanced statistical or machine learning models.
• Demonstrated ability and desire to obtain substantial domain knowledge in optimization, machine learning, and high-performance computing.
• Ability to communicate effectively with subject matter experts and to identify novel, impactful applications of optimization and artificial intelligence.
• Experience with one or more of the following areas of deep learning, such as large language models, graph neural networks, multimodal models, generative models, etc.

Desired Qualifications

• Experience with high-performance computing, GPU programming, and parallel programming, including running numerical simulations of complex workflows.
• Experience with optimization modelling languages such as Pyomo or JuMP and various optimization solver back-ends such Ipopt, Gurobi, Cplex, Mosek, etc.

All your information will be kept confidential according to EEO guidelines.

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years, open to those who have been awarded a PhD at time of hire date.

Why Lawrence Livermore National Laboratory?

• Included in 2024 Best Places to Work by Glassdoor!
• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
• Inclusion, Diversity, Equity and Accountability (IDEA) - visit https://www.llnl.gov/diversity
• Our core beliefs - visit https://www.llnl.gov/diversity/our-values 
• Employee engagement - visit https://www.llnl.gov/diversity/employee-engagement

Security Clearance

None required.  However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process.  This process includes completing an online background investigation form and receiving approval of the background check.  (This process does not apply to foreign nationals.) 

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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Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession.  This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.  

If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas.  Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.

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We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.

We invite you to review the Equal Employment Opportunity posters which include EEO is the Law and Pay Transparency Nondiscrimination Provision.

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Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory.  If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request. 

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