The N.C. Clean Energy Technology Center at N.C. State University is seeking a part-time assistant to support the Clean Transportation Program. This position is designed to be part-time, and we will work around a student’s academic schedule. Program assistants will support clean transportation research and modeling through data collection, data processing, data mining, and data visualization to extract insights
from energy and transportation datasets. Depending on the qualifications and interests of the candidate, program assistants could also support with database management, computational modeling, and mathematical programming.
Duties and Responsibilities
- Collecting, organizing, and aggregating data from multiple sources.
- Processing and transforming data as needed to prepare datasets for data mining.
- Exploring, clustering, and visualizing data to extract insights from it
- Presenting on your methods and findings through short presentations and/or short written briefs.
Work Schedule
Flexible; The assistant will establish a weekly schedule based on availability.
NCCETC expects the intern will work through Summer Session II with the option to continue work into the Fall 2024 semester.
Experience working with large datasets in one or more programming languages; Familiarity with basic data mining techniques related to extracting insights from large datasets; Basic familiarity with data clustering; Basic familiarity with data visualization.
Preferred Skills:
Basic understanding of electric vehicles (EVs) and EV charging infrastructure; Knowledge of data science and machine learning techniques; Research and/or literature review experience related to the fields of energy and/or transportation; Familiarity with computational modeling for energy and/or transportation systems; Experience with automating data retrieval from online sources; Experience working with and querying databases; Experience working with geospatial data formats (e.g., SHP, GDB, GeoJSON, etc.); Experience with mathematical programming / optimization; Experience with time series analysis.