Graduate PhD Student Intern (Summer) – Mathematical Optimization
National Laboratory of the Rockies · United States
- Hybrid
- Full-time
- $66,550 / year
- United States
Job highlights
- Optimize large-scale power systems planning with advanced algorithms.
- Deploy optimization solutions on high-performance computing systems.
- Develop approaches for transmission and capacity expansion.
- Collaborate with expert researchers on complex energy challenges.
- Contribute to publications and technical reports.
About the role
Graduate PhD Student Intern Mathematical Optimization
Location: CO - Golden
Position Type: Intern (Fixed Term)
Hours Per Week: 40
Working at NLR
NLR 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 AI, Learning and Intelligent Systems (ALIS) Group in the NLR Computational Science Center has an opening for a graduate student researcher in Mathematical Optimization for large-scale power systems planning. They will deploy developed optimization algorithms on DOE high-performance computing systems. The researcher will develop mathematically sound approaches for transmission and capacity expansion as applied to the bulk electricity systems to enhance economics, reliability, resilience, and security of bulk electric systems.
Responsibilities Include
- Develop and implement mathematically sound approaches for transmission and capacity expansion using distributed optimization methods on NLR’s HPC
- Collaborate with NLR researchers to assess tradeoffs between model detail and computational time
- Process and visualize results to inform algorithmic design
- Author, present and assist in the preparation of technical papers, reports and conference proceedings on topics related to power systems planning
Basic Qualifications
- Minimum of a 3.0 cumulative grade point average.
- Undergraduate: Must be enrolled as a full-time student in a bachelor’s degree program from an accredited institution.
- Post Undergraduate: Earned a bachelor’s degree within the past 12 months. Eligible for an internship period of up to one year.
- Graduate: Must be enrolled as a full-time student in a master’s degree program from an accredited institution.
- Post Graduate: Earned a master’s degree within the past 12 months. Eligible for an internship period of up to one year.
- Graduate + PhD: Completed master’s degree and enrolled as PhD student from an accredited institution.
Please Note
- Applicants are responsible for uploading official or unofficial school transcripts, as part of the application process.
- If selected for position, a letter of recommendation will be required as part of the hiring process.
- Must meet educational requirements prior to employment start date.
- Must meet educational requirements prior to employment start date.
Additional Required Qualifications
- Currently pursuing a PhD in applied mathematics, industrial engineering, chemical engineering, management science, operations research, or a related discipline
- Demonstrated experience with algebraic modeling, including the use of modeling tools such as Pyomo, JuMP, GAMS, AMPL, CPLEX, Gurobi etc.
- Demonstrated experience implementing algorithms with Python, Julia, or other major language
Preferred Qualifications
- Experience with Pyomo and/or JuMP.
- Experience with commercial and/or open-source optimization solvers (e.g., Gurobi, HiGHS, IPOPT).
- Experience with developing custom math-programming algorithms tailored to specific problems.
- Experience working with cross-disciplinary research teams
- Experience with mpi-sppy and/or progressive hedging
- Candidates should have demonstrated interest or experience in power systems planning and/or operations.
- Experience with publishing
- Experience with HPC workflows, bash script, linux etc.
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: / Annual Salary Range: $51,200 - $81,900
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 Summary
Benefits include medical, dental, and vision insurance; 403(b) Employee Savings Plan with employer match*; and sick leave (where required by law). 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. Internships projected to be less than 20 hours per week are not eligible for medical, dental, or vision benefits.
Based on eligibility rules
Badging Requirement
NLR 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. Intern assignments extending beyond six months will be subject to this requirement.
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
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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.
Key skills/competency
- Graduate PhD Student Intern Mathematical Optimization
- Mathematical Optimization
- Power Systems Planning
- Distributed Optimization
- HPC
- Python
- Julia
- Algebraic Modeling
- Pyomo
- Gurobi
Skills & topics
- Graduate Student
- PhD Intern
- Mathematical Optimization
- Power Systems
- Algorithm Development
- HPC
- Operations Research
- Industrial Engineering
- Applied Mathematics
- Python
- Julia
- Pyomo
- Gurobi
- Energy Research
- Internship
- Summer Intern
- Colorado
How to get hired
- Tailor your resume: Highlight your PhD research in mathematical optimization, experience with modeling tools (Pyomo, Gurobi), and programming skills (Python, Julia).
- Craft a compelling cover letter: Emphasize your interest in power systems and how your skills align with the job description.
- Prepare for technical questions: Be ready to discuss optimization algorithms, modeling techniques, and HPC environments.
- Showcase research aptitude: Demonstrate your ability to publish, present, and collaborate effectively with experienced researchers.
- Highlight academic success: Ensure your transcripts reflect a strong GPA and your enrollment in a relevant PhD program.
Technical preparation
Master advanced algebraic modeling techniques.,Implement algorithms using Python or Julia.,Familiarize yourself with optimization solvers.,Understand HPC workflows and Linux.
Behavioral questions
Describe a complex problem you solved.,How do you handle competing priorities?,Discuss a time you collaborated effectively.,How do you approach learning new technologies?
Frequently asked questions
- What are the specific educational requirements for the Graduate PhD Student Intern role at National Laboratory of the Rockies?
- To be eligible for the Graduate PhD Student Intern position, you must be currently enrolled as a full-time PhD student in a relevant field such as applied mathematics, industrial engineering, or operations research. You should have completed a master's degree and be within 12 months of its conferral, or have earned a bachelor's degree within the past 12 months if pursuing a direct PhD. A minimum GPA of 3.0 is also required. Applicants need to submit official or unofficial transcripts.
- What kind of experience is essential for this Mathematical Optimization internship at National Laboratory of the Rockies?
- Essential experience for this internship includes a strong academic background in applied mathematics or a related field, and demonstrated experience with algebraic modeling using tools like Pyomo, JuMP, GAMS, AMPL, CPLEX, or Gurobi. Proficiency in implementing algorithms with Python or Julia is also a key requirement.
- What are the preferred qualifications for the Graduate PhD Student Intern position at National Laboratory of the Rockies?
- Preferred qualifications include specific experience with Pyomo and/or JuMP, commercial or open-source optimization solvers (Gurobi, HiGHS, IPOPT), and developing custom math-programming algorithms. Experience working with cross-disciplinary research teams, publishing research, and familiarity with HPC workflows, bash scripting, and Linux are also advantageous.
- What is the salary range for the Graduate PhD Student Intern (Summer) – Mathematical Optimization role?
- The annual salary range for this full-time, 40-hour per week internship is between $51,200 and $81,900. The final salary determination will consider your education, training, experience, and other relevant factors.
- Does National Laboratory of the Rockies offer benefits to its interns?
- Interns may be eligible for certain benefits, including medical, dental, and vision insurance, and a 403(b) Employee Savings Plan with employer match. However, internships projected to be less than 20 hours per week are not eligible for medical, dental, or vision benefits. Specific eligibility for other benefits like relocation assistance or performance awards depends on the internship's terms.
- What is the application process for the Graduate PhD Student Intern role at National Laboratory of the Rockies?
- To apply, you must submit an application form for each position you are qualified for. This includes a cover letter and resume. You will also need to upload official or unofficial school transcripts as part of the application process. A letter of recommendation will be required if you are selected.
- How important is experience with power systems planning for this internship?
- While not strictly required, demonstrated interest or experience in power systems planning and/or operations is a preferred qualification. This indicates that candidates with this background may have a stronger application, as it aligns directly with the research focus of the role.
- What is the work environment like at National Laboratory of the Rockies for this internship?
- You will be joining the AI, Learning and Intelligent Systems (ALIS) Group within the NLR Computational Science Center. The environment is mission-driven, featuring state-of-the-art facilities, multidisciplinary research teams, and collaborations with industry and academia. The role involves working on large-scale power systems planning and deploying algorithms on DOE high-performance computing systems.