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Postdoctoral Researcher – Multi-resolution Transportation and Energy Modeling

Applications are closed

  • Job
    Full-time
    Entry Level
  • Research & Development
    Data
  • $73.2K - $120.8K
  • Golden
    Remote

Requirements

  • Must be a recent PhD graduate within the last three years.* Must meet educational requirements prior to employment start date.
  • Preferred Qualifications:
  • Familiarity with transportation data and development of travel modeling and forecasting tools.
  • Experience with transportation modeling packages such as CUBE, TransCAD, MATSim, or similar tools.
  • Strong background with scientific programming languages such as Python, R, Rust, or Scala.
  • Strong interest in, and working knowledge of high-performance computing and transportation modeling, with a degree/background in one or the other.
  • Relevant transportation skills may include travel demand modeling, micro-, meso- and/or macroscopic traffic modeling, and transportation energy analysis.
  • Relevant computational skills may include machine learning, deep learning, distributed computing, and big data management/analytics.
  • Familiarity with location-based data, and its use for travel modeling.
  • Knowledge of GIS, GPS, data analysis and visualization, web design, and/or engineering computing.
  • Strong collaboration skills—thrives working in a collaborative team environment but is comfortable working independently when necessary.
  • Additional preferred qualifications:
  • Excellent verbal and written communication skills.
  • Experience with data analytics, big data management, scraping data off the web, high-performance/distributed computing, machine learning and/or deep learning.
  • Application development experience in a general-purpose programming language such as C/C++, JavaScript, Java, Scala, or Rust.
  • Experience interfacing with Metropolitan Planning Organizations (MPOs) and other transportation data and modeling stakeholders.
  • Knowledge of cloud computing services such as AWS, Azure, or Google Cloud.
  • Working with HPC and databases, including PostGres, and slurm.
  • Experience in transportation and/or computational sciences.
  • Experience with collaborative software development and use of Git/GitHub for project management and version control.
  • Knowledge of access theory.
  • Cumulative undergraduate/graduate GPA over 3.5 on a 4.0 scale.

Responsibilities

  • Working with travel demand models, particularly integrating NREL developed tools such as the Mobility Energy Productivity (MEP) metric, and Route Energy Prediction Model (RouteE) into existing travel demand models.
  • Developing travel choice models using traditional (survey) and non-traditional (big-data) sources.
  • Perform big data analytics, visualization, and interpretation.
  • Working with cloud-based web applications and scalable data analysis pipelines, refactoring data science scripts as cloud-native software.
  • Building, enhancing, and validating modeling and simulation tools.
  • Assist in producing high-quality publications, reports, and research proposals.
  • Supporting additional related research projects as needed.
  • .

Application Process

  • Please include all relevant experience and qualification information on the uploaded PDF (or MS Word) copy of your resume or CV.

Science & Healthcare
Industry
1001-5000
Employees
1977
Founded Year

Mission & Purpose

The National Renewable Energy Laboratory (NREL), a Department of Energy national lab, is #TransformingEnergy as the nation's primary laboratory for renewable energy and energy efficiency research and development. NREL's Mission: NREL develops renewable energy and energy efficiency technologies and practices, advances related science and engineering, and transfers knowledge and innovations to address the nation's energy and environmental goals. NREL's Strategy: NREL has forged a focused strategic direction to increase its impact on the U.S. Department of Energy's (DOE) and our nation's energy goals by accelerating the research path from scientific innovations to market-viable alternative energy solutions.