R&D Associate Staff Member - Transportation Analysis for Emerging Mobility Technologies
Oak Ridge National Laboratory
Location: Knoxville, Tennessee
Type: Full Time
Years of Experience:
2 - 4
The Oak Ridge National Laboratory, Transportation Analytics & Decision Sciences Group (TADS) is seeking to fill an Associate Research Staff position with a focus on emerging mobility technologies.
The successful candidate will be able to work with a research team from various backgrounds to model and understand the impacts and potential adoption rates for transportation technologies such as smart and shared mobility, electric vehicles, intelligent transportation systems, self-driving vehicles, and connected vehicles.
TADS goal is to find sustainable multi-modal (air, land, water, pipelines) solutions to passenger and freight transportation challenges within the broad context of environmental, social, and economic goals. Research teams apply statistical and econometric modeling, machine learning, simulation, and optimization methods to data-intensive analyses. The resulting data sets and models seek to illuminate historical performance of the transportation system with multiple metrics, to predict future performance under various scenarios, to understand the factors that influence the adoption of new technologies, and to search for optimal pathways to sustainable futures.
Job Duties and Responsibilities:
You will work with staff and independently in ORNL’s National Transportation Research Center to research the impacts of transportation technologies, such as electric vehicles, automated vehicles, and shared mobility.
Specific activities may include:
Identify and analyze large-scale transportation and demographics datasets to inform developments of transportation system models
Apply advanced econometrics and statistical modeling to discover insights on costs, performances, and market dynamics of emerging mobility technologies
Apply big data analytics to infer patterns from large-scale and separate demographics, behavior, and sensor data sets
PhD in transportation engineering or related fields
Engineering background with strong quantitative skills
At least two years in an applied transportation analysis position.
Transportation modeling and simulation, data mining, machine learning, optimization, and statistics
Strong publication record
Strong programming capabilities in Visual Studio, C#, Python, R and other platforms
Data-driven research experience in emerging mobility technologies, including smart and shared mobility, electric vehicle, intelligent transportation systems, self-driving vehicles, connected vehicles
This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.
We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.
If you have trouble applying for a position, please email [email protected]
ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.