Mathematical Optimization Jobs (NOW HIRING)

Posting TitleResearcher III - Mathematical Optimization for Energy Systems

.

LocationCO - Golden

.

Position TypeRegular

.

Hours Per Week40

.

Working at NLRNLR is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development.

Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.

At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.

Job Description

The Advanced Computing Solutions Group in the NLR Computational Science Center has an opening for a Computational Science Researcher, with an emphasis on mathematical optimization and its application to the design and control of energy systems. We are looking for a dynamic researcher with a strong technical background to help us transform our energy future through advanced automation, control and decision making.

The successful candidate will have extensive experience with mathematical optimization formulations and algorithms and their application to physical systems. Additionally, the candidate will be familiar with parallel algorithmic approaches for large-scale linear, nonlinear, integer, and stochastic optimization problems. We anticipate that the research will involve integrating Artificial Intelligence (AI) techniques, such as reinforcement learning (RL), with classical mathematical optimization approaches and implementations. We seek candidates capable of pursuing research directions that combine these algorithmic components, using implementations that are suitable for effective utilization of the modern parallel computing architectures that are available at NRL. Candidates with creative problem-solving skills, interest in cross-disciplinary collaboration, and a passion for the mission and goals of both NLR and CMEI are of particular interest.

Responsibilities:

• Collaborate with domain experts to identify where mathematical optimization constitutes a viable approach and maintain awareness of optimization-related research both at NLR and in the literature more generally.
• Adopt existing - or develop new - mathematical, computing, and simulation frameworks required to implement and evaluate the performance of optimization algorithms and solutions.
• Creatively identify new opportunities to leverage AI/RL to augment or enhance classical optimization algorithms and/or formulations.

• Author publications and contribute to proposals to sustain research directions.

.

Basic QualificationsRelevant PhD . Or, relevant Master's Degree and 3 or more years of experience . Or, relevant Bachelor's Degree and 5 or more years of experience . Demonstrates complete understanding and wide application of scientific technical procedures, principles, theories and concepts in the field. General knowledge of other related disciplines. Demonstrates leadership in one or more areas of team, task or project lead responsibilities. Demonstrated experience in management of projects. Very good technical writing, interpersonal and communication skills.

* Must meet educational requirements prior to employment start date.

Additional Required Qualifications

• Good understanding of optimization fundamentals, both computational and mathematical.
• Experience programming in Python and/or Julia
• Experience with Pyomo and/or JuMP
• Experience with mathematical optimization solvers, e.g., CPLEX, Gurobi, Xpress, Cbc, Ipopt, and their capabilities.
• Familiarity with distributed computing frameworks such as MPI and OpenMP
• Experience with scalable machine learning frameworks, e.g, PyTorch
• Experience building foundation models for AC-OPF on transmission grids
• Experience with using machine learning and signal processing techniques for fault detection on microgrids

Preferred Qualifications

• Experience working with diverse, inclusive, and cross-disciplinary research teams
• Experience working on HPC systems

.

Job Application Submission Window

The anticipated closing window for application submission is up to 30 days and may be extended as needed.

Annual Salary Range (based on full-time 40 hours per week)Job Profile: Researcher III / Annual Salary Range: $83,600 - $150,500

NLR takes into consideration a candidate's education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee's salary history will not be used in compensation decisions.

Benefits SummaryBenefits include medical, dental, and vision insurance; short*- and long-term disability insurance; pension benefits*; 403(b) Employee Savings Plan with employer match*; life and accidental death and dismemberment (AD&D) insurance; personal time off (PTO) and sick leave; paid holidays; and tuition reimbursement*. NLR employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component. Some positions may be eligible for relocation expense reimbursement. Limited-term positions are not eligible for long-term disability or tuition reimbursement.

* Based on eligibility rules

Badging RequirementNLR is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as required by Homeland Security Presidential Directive 12 (HSPD-12), which includes a favorable background investigation.Drug Free Workplace

NLR is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug.

If you are offered employment at NLR, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.

Submission Guidelines

Please note that in order to be considered an applicant for any position at NLR you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.

.

Equal Opportunity Employer

All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.

Reasonable Accommodations

E-Verify www.dhs.gov/E-Verify For information about right to work, click here for English or here for Spanish.

E-Verify is a registered trademark of the U.S. Department of Homeland Security. This business uses E-Verify in its hiring practices to achieve a lawful workforce.