We are seeking a postdoctoral appointee to support the development of large-scale modeling, simulation, and analysis platforms in the areas of energy, military, transportation, and supply chains. Successful candidates will work closely with other team members to gather requirements, design and implement software for complex modeling, simulation, and data analysis, and deliver detailed documentation for the analytical products. Security clearance-eligible candidates with a desire to apply advanced computational methods to logistics and modeling problems on a global scale are encouraged to apply.

The Modeling and Analytics group at Argonne National Laboratory is dedicated to applying decision science principles to address some of the nation’s most pressing logistical challenges. We specialize in developing computational tools for the Department of Energy (DOE), the Department of Defense (DOD), and the Department of Homeland Security (DHS). Our diverse team of experts work together across interdisciplinary boundaries to design and implement cutting-edge algorithms, models, and simulations that can help decision-makers understand complex systems and make informed decisions. Our group is part of Argonne’s Strategic Security Sciences Division, which researches, develops, and implements practical approaches and technical solutions to address complex and enduring threats to national and global security.

Argonne National Laboratory is a multidisciplinary science and engineering research center, where talented scientists and engineers work together to answer the biggest questions facing humanity, from how to obtain affordable clean energy to protecting ourselves and our environment. Ever since we were born out of the University of Chicago’s work on the Manhattan Project in the 1940s, our goal has been to make an impact — from the atomic to the human to the global scale.

Key Responsibilities:

• Collaborate closely with the project team to develop transportation modeling and simulation software.

• Maintain cutting-edge knowledge of advances in modeling, simulation, machine learning, and data analysis.

• Employment of these models on high performance computers and cloud-based infrastructures.

• Design, develop, and deploy novel business intelligence and machine learning applications.

• Manage data at scale.

• Publish papers in high-quality refereed conferences and journals.

• Actively collaborate with industry, academia, government labs, and applications developers in a variety of venues.

This position will be hybrid, with on-site presence at Argonne’s Lemont, IL campus required 3 days each week.

Position Requirements

• This level of knowledge is typically achieved through a formal education in computer science, mathematics, or a related field at the PhD level with zero to five years of employment experience.

• The candidate must have the ability to independently derive research insights to bring them to fruition through publication or demonstration on mission applications.

• Applicants should demonstrate excellent interpersonal skills, oral and written communication skills.

• The ability to work in a dynamic, interdisciplinary team.

• Programming expertise in Java, C++, Python, or R. Experiences with large databases and data manipulation tools is preferred.

• Research experience in one or more of the following areas: 1.) Complex systems modeling, including simulation or analytical modeling, 2.) High performance computing, parallel programming, or cloud computing, 3.) Data science, data analysis, business intelligence, or machine learning.

• A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork. 

• Ability to make our laboratory a safe, welcoming, inclusive, and accessible environment where all can thrive.

• Selected candidate will be required to obtain and maintain a clearance and must be a U.S. citizen. Please see http://www.state.gov/m/ds/clearances/c10978.htm for more information about requirements for obtaining a security clearance process.

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