Postdoctoral Researcher - Mathematical Optimization Energy Systems

Position: Postdoctoral Researcher - Mathematical Optimization for Energy Systems
Posting Title

Postdoctoral Researcher - Mathematical Optimization for Energy Systems

.

Location

CO - Golden

.

Position Type

Postdoc (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 Advanced Computing Solutions Group in the NLR Computational Science Center has an opening for a full-time Postdoctoral Researcher - Computational Sciences, 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 didates 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 Qualifications

Must be a recent PhD graduate within the last three years.

* Must meet educational requirements prior to employment start date.

Additional

Required Qualifications

* Experience formulating optimization problems in an algebraic modeling language, e.g., Pyomo, JuMP, PuLP, GAMS.

* Experience with mathematical optimization solvers, e.g., CPLEX, Gurobi, Xpress, Cbc, Ipopt, and their capabilities.

* Good understanding of optimization fundamentals, both computational and mathematical.

Preferred Qualifications

* Familiarity with distributed computing frameworks such as MPI and OpenMP

* Experience with Pyomo and/or JuMP

* Experience programming in Python and/or Julia

* Experience with scalable machine learning frameworks, e.g, Py Torch

* Experience working with diverse, inclusive, and cross-disciplinary research teams

.

Job…