Job Title: Postdoctoral Fellow in Geospatial Data Analytics

Research Objective

This project aims to leverage artificial intelligence technologies to automate, enhance, and generate detailed reports that analyze and present school transportation data in various formats. By utilizing AI-driven reporting, the process of collecting, interpreting, and visualizing this data will be significantly streamlined, enabling school districts to gain critical insights more quickly and efficiently. The transportation dataset will encompass a comprehensive array of information, including student records, vehicle details, school data, route information, staff profiles, vehicle GPS locations, vehicle dispatch histories, student attendance and ridership, vehicle maintenance and inspections, parent communication histories, and more.

The project will utilize natural language processing (NLP) to generate reports based on user input, offering flexibility in report creation. These reports will be available in a range of formats, such as CSV, Excel, text, Word, and PDF. Additionally, the integration of visual aids like pie charts, bar charts, and other graphical representations will further enhance the clarity and accessibility of the data, making it easier for stakeholders to interpret and act on the insights provided. By automating these processes, the project seeks to improve data accessibility, reduce manual effort, and provide actionable insights to support decision-making in school transportation management.

Position Duties

Project Management (20%)

●     Serve as the point of contact for the project team with industry sponsors.

●     Mentor student interns and fellows, assess their competencies and assign appropriate tasks for each, monitor their progress, and provide support as needed.

●     Coordinate project team meetings, build and update project plans, produce and present project reports to PI and sponsors.

Development (60%)

·         Develop workflows-based data integration solutions that provide standardized data integration and efficient and timely data updates.

·         Evaluate NLP models and select the best model to build an AI-powered user query and reporting system aimed at serving a diverse user base, including school management, routing administrators, and student parents.

·         Design and build a comprehensive dashboard including charts, tables, figures, maps and text for effective communication.

·         Design a secure database management system that protects student privacy and system security.

Training and documentation (20%)

·         Provide technical training to clients and users as well as fellow researchers and student interns.

·         Produce documentation for developers and public users.

·         Lead manuscript writing for peer-reviewed journals.

·         Deliver presentations for the research community in project meetings and academic conferences.

Position affiliation

The position is sponsored by the NSF Industry–University Cooperative Research Centers (I/UCRC) Program in the Spatiotemporal Innovation Center at the Center for Geographic Analysis (CGA) of Harvard University. The candidate will have office space and a computer assigned at the CGA and attend group meetings and related activities with other research sites and collaborating sponsors.

This is a full-time, benefits-eligible position. Compensation is $67,600 per year. The appointment is for one year with the possibility of renewal based on satisfactory performance and continued availability of funding.

CGA is a member organization under the Institute for Quantitative Social Science (IQSS). It was established in 2006 to support research and teaching across all disciplines in the University as they relate to geospatial technology and methods. Combining consultation services, technical training, platform development and sponsored research, the Center enables a diverse range of research projects involving geospatial analysis.

IQSS sits in the Division of Social Science, which is strongly committed to creating and supporting a diverse workforce. Respect and fairness, kindness and collegiality, and trust and transparency are among the values we espouse and promote in our workplace culture. We work hard to ensure a healthy, inclusive and positive environment where everyone does their best work in support of Harvard’s mission.

Basic Qualifications

Doctoral degree in geographic information science or a related field.

PLEASE NOTE: If you have obtained your Ph.D. in the past 12 months you must be able to provide a certificate of completion from the degree-granting institution OR a letter from the institute’s registrar stating all requirements for the degree have been successfully completed and should verify the date the degree has been or will be conferred. No exceptions.

Additional/Preferred Qualifications

·         Proficiency in programming languages commonly used in AI research, such as Python, R, or Julia.

·         Experience with AI frameworks and libraries like TensorFlow, PyTorch, Keras, or Scikit-learn.

·         Knowledge of algorithms, data structures, and statistical methods relevant to AI and machine learning.

·         Expertise in specific areas of AI, such as natural language processing, computer vision, reinforcement learning, or deep learning.

·         Strong skills in designing experiments, analyzing data, and interpreting results.

·         Ability to apply advanced machine learning techniques and theoretical concepts to solve complex problems.

·         Excellent written and verbal communication skills for presenting research findings and collaborating with interdisciplinary teams.

·         Experience in managing research projects, including time management, resource allocation, and milestone tracking.

Application Procedure

To ensure full consideration, complete applications must be received by November 20, 2024.

Employees will have federal and MA state taxes withheld.

J-1 Research Scholar visa sponsorship may be available in limited circumstances to individuals already in the US on a visa, depending on their specific immigration status.

For more information, please visit https://academicpositions.harvard.edu/postings/14263.