The Energy Systems and Infrastructure Analysis (ESIA) division addresses energy security and environmental sustainability by analyzing the benefits and challenges of efficient energy technologies and making U.S. energy systems more sustainable and resilient. We develop and use analytic tools to help the U.S. achieve these goals. We also develop, deploy, and advance grid technologies that ensure a robust and secure U.S. grid transmission and distribution system. We collaborate with government agencies as well as companies to help move the nation toward an economy based on affordable and sustainable energy, including solar, wind, and hydropower energy.

The successful postdoctoral candidate will join a team of Argonne researchers and work closely with federal agencies and other national laboratories. The successful candidate will support technical, economic, and environmental analyses of power systems with a particular focus on hydropower systems. The successful candidate will develop and apply methodologies and tools for financial, economic, and environmental analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and financial models. The position will include the analysis of hydropower operation and expansion, optimization and equilibrium, market penetration, and interdependencies. This description documents the general nature of work but is not intended to be a comprehensive list of all activities, duties and responsibilities required.

The successful postdoctoral candidate will develop power systems and hydropower modeling tools that support energy, economic, and environmental analyses. Some of these tools will require the creation of simulation and optimization routines.

The successful candidate will perform data analytics and conduct research related to a wide range of topic areas including the analysis of energy and power systems, electricity market design and operations, hydropower energy and energy storage integration, and other energy technologies.

The position involves presenting and publishing results in peer reviewed reports and journal articles, making presentations at conferences, supporting program development activities, and maintaining up-to-date knowledge of pertinent literature in which related work is being conducted.

Position Requirements

• PhD degree in engineering, operational research, computer science, or a related field.

• Knowledge of mathematical optimization methods (e.g., Gurobi, CPLEX, linear programming, mixed integer linear programming, or stochastic programming) and solvers (e.g. CPLEX, Gurobi).

• Experience using data science programming languages such as Python, Julia, MatLab, R, or C/C++.

• Knowledge of power systems operations, production cost models, electricity markets, ISO and utility business, applied economics, policy, and regulations.

• Experience in statistics and statistical analysis.

• Knowledge of basic financial and economic principles.

• Ability to independently identify and solve challenging technical problems and also work effectively in a multidisciplinary team environment.

• Record of productive and creative research, demonstrated by publications in peer-reviewed journals and presentations at scientific conferences.

• Skilled written, verbal, and interpersonal communication skills at all levels of the organization.

• Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork. 

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As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.

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