What We Offer?

Productive Environment

Interdisciplinary Collaborations

High Research Impact

Diverse and Fun Teams

Competitive Pay

Join the team!

At Social AI Studio, we aim to develop and train the future generation of social AI scientists/designers. Email us your CV!

  • Postdoctoral Research Fellow: Candidate should have a Ph.D. degree and a promising academic record. Topics considered particularly relevant include social media mining, computational social science, machine learning, deep learning, recommender systems, natural language generation. A strong background in other computational social science, data mining and machine learning topics will also be considered.

  • Research Engineer: Candidate should have a Bachelor’s and/or Master’s degree in Computer Science or closely-related disciplines. He or she should be competent in at least one programming language and have some experience in implementing data mining algorithms and machine learning models (school projects included).

  • Master/Ph.D. Student: Candidate should have a Bachelor’s and/or Master’s degree in Computer Science or a closely-related discipline with high academic standing. He or she should be competent in at least one programming language and demonstrate a strong passion for AI and/or computational social science research. We also offer scholarships to outstanding students enrolled in our Ph.D. programs!

  • Research Intern: Candidate should be currently enrolled in a Bachelor’s and/or Master’s degree in related disciplines. He or she should have a keen interest to learn more about AI and/or computational social science research.

Projects Hiring

The following funded projects are hiring Postdoctoral Research Fellows, Research Engineers, and Research Interns:

  1. Detecting and Monitoring Hate Speeches in Social Media - This proposal aims to design effective data analytical tools and machine learning algorithms to curb the spread of hate speech on social media. Specifically, we outline two main aims of this project: (i) Develop semi-supervised meta-learning algorithms to detect hate speech on multiple social media platforms. (ii) Designing multilingual analytical monitoring dashboards that monitor hateful social media content across Southeast Asian (SEA), interpreting content in native southeast Asian languages.

  2. Memenlytics: Understanding Memes in Social Media - The proposed project aims to improve our understanding of memes shared in online communities. More specifically, the project aims to design state-of-the-art deep learning algorithms and analytical tools that enable large-scale analysis of online memes. In addition, the developed algorithms and analytical tools will also support the analysis of memes based on Singapore context (e.g., expressing views towards Singapore government bodies and policies).

  3. Authorship Attribution on the Web - Authorship attribution (AA) is the task of determining the writer of an article. This proposed project aims to develop AA technologies for the content shared online. Specifically, the project aims to design state-of-the-art deep learning algorithms accompanied with analytical tools that empower analysts to perform AA on large-scale online generated content in local contexts. Besides making technical contributions, we aim to provide insights to empower analysts and platform providers to sieve out sock puppets from authentic authors. These new insights enabled by our developed AA techniques can also be used to design policies to foster a more cohesive online community.

  4. Text Analytics & Auto Reply - This project is funded by URA and it aims to design and develop natural language processing systems to support urban planning. Specifically, we will analyze and classify urban planning feedback from public users and extract relevant information for future urban planning. We will also design text generation systems that could automatically reply and respond to user feedback. This is an interdisciplinary project where the candidates will be working closely with architectural researchers and urban planners!

  5. ML techniques for automated understanding of medical records - This project is supported by ASUS AICS via the Economic Development Board Industrial Postgraduate Programme (EDB IPP). AICS is a newly founded division in ASUS focusing on AI software development. Asus AICS has been developing various solutions for healthcare that utilize the latest advancements in machine learning, computer vision and natural language understanding. We aim to recruit and fund two PhD students under this project. The PhD students will receive a full scholarship to design new natural language understanding (NLU) algorithms for healthcare and medical applications.