Transportation Systems Intern (Year-Round) at National… · Golden

Summary: National Laboratory of the Rockies (NLR) is the nation's primary laboratory for energy systems research and development, located in Golden, Colorado. They are seeking a Transportation Systems Intern who will assist in supporting large-scale modeling efforts for charging station impact analysis and developing AI-based traffic operation algorithms. Responsibilities: - Assist in developing Reinforcement-Learning-based traffic control algorithms - Collaborate with NLR researchers to build high-fidelity traffic simulations - Support development of synthetic population, demand modeling, and total cost of ownership workflows - Process and analyze large-scale transportation datasets - Process and visualize results from simulations scenarios to inform real-time traffic operations and planning decisions - Author, present and assist in the preparation of technical papers, reports and conference proceedings on topics related to data-driven traffic modeling and simulation and their application Required Qualifications: - Minimum of a 3.0 cumulative grade point average - Graduate: Must be enrolled as a full-time student in a master's degree program from an accredited institution - Post Graduate: Earned a master's degree within the past 12 months. Eligible for an internship period of up to one year - Graduate + PhD: Completed master's degree and enrolled as PhD student from an accredited institution - Strong programming skills in Python - Experience with data analysis and scientific computing - Experience working with large datasets - Strong analytical and problem-solving skills - Excellent written and verbal communication skills - Ability to work independently and collaboratively Preferred Qualifications: - Would prefer an intern candidate who is specifically in a PhD program - Experience with transportation modeling or transportation systems analysis - Experience with HPC environments (Slurm, clusters, parallel computing) - Experience with machine learning and reinforcement learning - Experience working with synthetic population and travel demand modeling - Experience with SUMO Required Skills: Python programming, Data analysis, Scientific computing, Large dataset handling, Transportation modeling, Transportation systems analysis, High Performance Computing (HPC) environments, Machine learning, Reinforcement learning, Synthetic population modeling, Travel demand modeling, SUMO traffic simulation, Analytical skills, Independent work, Collaborative work Benefits: Medical, dental, and vision insurance, 403(b) Employee Savings Plan with employer match, Sick leave (where required by law), Performance-, merit-, and achievement- based awards that include a monetary component, Relocation expense reimbursement