Research Assistant Position: Data Science for Housing Research

We are seeking a talented and motivated Research Assistant to join an innovative project leveraging Large Language Models (LLMs) to analyze thousands of hours of Vancouver City Council meeting recordings. The project aims to unpack the housing development approval process and explore the utility of LLMs for social science research.

The project is co-led by Dr. Julia Harten (School of Community and Regional Planning) and Dr. Christos Thrampoulidis (Electrical and Computer Engineering).

Project Overview

Housing affordability is a critical issue. This research project aims to:

• Understand why housing development proposals succeed or fail.
• Leverage LLMs to analyze public records of city council meetings (text and video).

Responsibilities

The Research Assistant will help manage a computational pipeline for processing and analyzing city council meeting data. Key tasks include:

• Building and evaluating LLM-based algorithms for text classification, including conversation-level analysis of linguistic style.
• Conducting topic modeling and regression analysis.
• Collaborating with Principal Investigators and team members.
• Contributing to publications and presentations of research findings.
• Conducting literature reviews on relevant topics.

Qualifications

Required

• Enrolled in a graduate degree program in Computer Science, Data Science, Urban Planning, or related fields (advanced and motivated undergraduate students will also be considered).
• Strong experience in managing large datasets, data processing, and cleaning.
• Proficiency in PyTorch with experience in deep learning applications.
• Excellent programming skills, particularly in Python.
• Strong analytical and problem-solving skills.
• Ability to work both independently and as part of a collaborative research team.

Preferred

• Experience with open-source LLMs and their APIs.
• Background or knowledge in urban planning, housing policy, or related social sciences.
• Experience with interdisciplinary research project

Position Details

• Commitment: Minimum of 10 hours/week (flexibility for additional hours).
• Compensation: Competitive salary based on experience, including funding for conference travel.
• Start Date: Available immediately (start date flexible for the right candidate).

Application Deadline

• Applications will be reviewed on a rolling basis, and the position will remain open until filled.