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Computational Transportation Engineer

1 month ago

Lemont, United States Argonne National Laboratory Full time

The Transportation Systems and Mobility Group in the Vehicle and Mobility Systems Department at Argonne is seeking a transportation systems modeler to work on the high-performance agent-based transportation system simulation framework, POLARIS. The model is used to perform detailed simulations of large-scale regional transportation systems. The goals of such simulations are to analyze traveler responses to demographic dynamics, emerging land uses, and new mobility technologies such as vehicle electrification, shared autonomous vehicle systems, new freight delivery technologies and others. We seek to understand the potential impacts and radically improve the mobility and energy sustainability of transportation. Decision makers at the federal, state, and regional levels rely on such simulation models to make decisions on investments and management strategies. The core algorithm of the model includes activity-based travel demand modeling that utilize decision-choice theories regarding travelers' activities and associated travel pattern and inform how the underlying process orientation of such choice decisions may be affected by intelligent transportation system technologies, and emerging mobility alternatives such as connected, automated, shared, and electric mobility, among others. This position will focus on developing theories and models of human decision making and travel behavior to improve integrated travel demand modeling simulations and extend knowledge of travel behavior to future technology and policy scenarios. It will also focus on developing mathematical and computational techniques to improve the capabilities and performance of state-of-the-art land use, travel demand and traffic simulation codes and incorporate new models of land use and travel behavior within the POLARIS modeling tool.

Position Requirements

  • Knowledge in travel behavior analysis, activity-based travel demand modeling, and discrete choice and other decision process models
  • Knowledge of engineering computation as applied to transportation system modeling and planning.
  • Skills and experience in software programming (e.g., Python, C++).
  • Skills in decision making.
  • Skills to roadmap from specific problems to general solutions.
  • Skilled in the use of high-performance computer technology.
  • Ability to plan, schedule, and coordinate detailed phases for projects of moderate scope.
  • Skilled oral and written communication skills at all levels of the organization.
  • A successful candidate must have the ability to model Argonne's Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
The selected candidate will be placed at the appropriate level (RD2 or RD3) depending upon depth and breadth of relevant knowledge, skills, and experience brought to the position. The requirements for the desired levels are as follows:
  • RD2 Level: Bachelor's and 5+ years; Master's and 3+ years; Doctorate and 0 years, or equivalent
  • RD3 Level: Bachelor's and 8+ years; Master's and 5+ years; or Doctorate and 4+ years; or equivalent.
Preferred Qualifications:
  • Knowledge in agent-based modeling and simulations, familiarity with POLARIS.


Job Family
Research Development (RD)

Job Profile
Computational Science 2

Worker Type
Long-Term (Fixed Term)

Time Type
Full time

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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