Research Assistant (Online Trust and Safety)
Introduction
The Department of Communications and New Media (CNM) at the National University of Singapore is seeking a Research Assistant with a background in Computer Science or Data Science to support a project funded by the Online Trust and Safety (OTS) Research Programme. The project develops multilingual corpora, annotation pipelines, and severity detection models for online harm in Singapore's digital spaces.
Job Description
The Research Assistant will contribute to the technical infrastructure of the project across four phases. Phase 1 involves developing automated data collection scripts and tools for extracting content from Singaporean online platforms (Facebook, Instagram, X/Twitter, Reddit) using licensed APIs. Phase 2 involves data cleaning, preprocessing, and corpus management. Phase 3 involves machine learning experiments, including fine-tuning large language models (e.g., Llama 70B) for harmful content annotation and developing multimodal toxicity detection pipelines. Phase 4 involves tabulation of results, preparation of figures, and contribution to technical reports and papers.
Qualifications
- Minimum Bachelor's Degree in Computer Science, Data Science, or a closely related field
- Proficiency in Python and familiarity with NLP libraries (Hugging Face Transformers, spaCy, or equivalent)
- Experience with large language models, fine-tuning, and/or multimodal machine learning
- Familiarity with data collection via APIs and web scraping tools
- Knowledge of statistical analysis and experience working with large unstructured datasets
- Excellent written and verbal communication skills.
We regret to inform that only shortlisted candidates will be notified.
How to Apply
A full application should consist of the following:
- cover letter, highlighting the applicant’s interest in the projects’ topics and commitment to diversity
- full curriculum vitae, including the candidate’s academic history, relevant research experience (if any), and list of publications (if any)
- one academic writing sample (up to 25 pages)
- at least three (3) names for letters of recommendation. If the applicant is shortlisted, recommender(s) will be approached for their letter(s).
Inquiries about the position may be directed to Dr Kokil Jaidka at jaidka@nus.edu.sg.
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