R&D Associate Staff Member - Data Analytics for Traffic Modeling
Oak Ridge National Laboratory
Location: Knoxville, Tennessee
Type: Full Time
Years of Experience:
2 - 4
At Oak Ridge National Laboratory, we provide extraordinary researchers with distinctive equipment and unique facilities to tackle some of the nation’s most compelling challenges. As the largest US Department of Energy (DOE) open science laboratory, our mission is to deliver scientific discoveries and technical breakthroughs that will accelerate the development and deployment of solutions in clean energy and global security while creating economic opportunities for the nation.
The Transportation Analytics and Decision Sciences (TADS) Group within the Buildings and Transportation Science Division (BTSD) at the Oak Ridge National Laboratory is seeking applicants for the position of Associate Research Staff. The TADS Group assists the U.S. Department of Energy (DOE), Department of Transportation (DOT), and other federal agencies in developing solutions to national transportation problems.
TADS researchers conduct spatial- and statistical-analyses, build computer simulation and optimization models, as well as carry out engineering and economic analyses of issues pertaining to the transportation sector. This position will primarily support a multi-disciplinary effort that heavily focuses on freight and passenger transportation systems, planning, modeling, and big data analysis. The purpose of the position is to perform research and development (R&D) in areas such as traffic modeling, signal optimization, freight facility congestion, and urban air mobility.
Major Duties / Responsibilities:
You will function as a team member with a group of scientists and engineers and collaborate in performing applied R&D and development of models for informed transportation policy and planning decision-making. You will develop traffic models at appropriate scales for various problems. These can range from agent-based models of individual vehicles to higher level flow models. Modeling the integration of intelligent autonomous vehicles or advanced traffic signal controls is a key component of improving traffic flow and energy conservation.
Working as a team member with experienced researchers, you will be expected to contribute to formulating research problems and designing research strategies, conducting experimentation and supportive analyses using state-of-the art methods. A strong background in transportation engineering, big data analytics, and machine learning is essential.
The production of research articles in high quality journals is a critical part of the job and a component of performance evaluation.
Specific job responsibilities and roles include:
Work independently to perform applied research and development in modeling and analyzing national and regional freight and passenger movements, and in providing necessary deliverables such as documents, data files and tools on time.
Collaborate with senior research staffs to provide assistance with performing energy and transportation related projects conducted for DOE, DOT, and other federal agencies.
Represent ORNL and make technical presentations to sponsors, technical merit reviewers, and at national/international technical conferences and symposia.
Publish original research in peer-reviewed journals and other professional technical publications.
Adhere to ORNL’s culture of safety in all aspects of work performance and general conduct.
Some travel may be required.
Ph.D. in transportation engineering, civil engineering, or related discipline, with a recognized record of research accomplishments.
At least 2 years of experience in transportation system modeling and simulation of advanced connected vehicles.
Proficiency in the use of modeling and simulation tools, such as SUMO.
Experience with machine learning techniques including neural networks.
Excellent oral and written communication skills to support regular interactions with ORNL staff, management, sponsors, and others, and to prepare reports, publications, and journal articles.
Strong interpersonal skills to support team building and leadership. This includes ability to both advise and learn in multi-disciplinary environment with staff from diverse technical backgrounds.
Demonstrated initiative and ability to learn and provide creative approaches in performing technical projects and solving technical challenges.
Prior publication record
Involvement with professional societies
Experience in private industry
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 ORNLRecruiting@ornl.gov.
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.