Researcher III - Mathematical Optimization for Energy Systems
Role details
Job location
Tech stack
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., * 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.
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Adopt existing - or develop new - mathematical, computing, and simulation frameworks required to implement and evaluate the performance of optimization algorithms and solutions.
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Creatively identify new opportunities to leverage AI/RL to augment or enhance classical optimization algorithms and/or formulations.
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Author publications and contribute to proposals to sustain research directions.
Requirements
Do you have experience in Technical writing within technology?, Do you have a Master's degree?, Relevant 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., * 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, * Experience working with diverse, inclusive, and cross-disciplinary research teams
- Experience working on HPC systems
Benefits & conditions
Pulled from the full job description
- 403(b) matching
- Tuition reimbursement
- 403(b)
- AD&D insurance
- Health insurance
- Paid time off
- Vision insurance, Benefits 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.