Research Assistant – Statistical Programming and Literature Review

1. (FASS/CBME) Under the grant “DebiasSING: Finding and Fixing Biases in the Human-AI Complex”, the co-PIs aim to address biases in large language models and its interaction with potentially biased human users, which is an urgent and dynamic policy challenge. The study will involve conducting evaluations of different large language models, as well as survey experiments of humans using large language models, in Singapore and elsewhere. (More details of the grant can be found here: https://www.ssrc.edu.sg/projects/thematic-grant/lorenz2025/)
2. (BIZ) Under Dr Lee Kwok Hao’s startup grant, the co-PI(s) plan to examine emerging issues at the intersection of algorithmic guidance and management. These issues have become more pressing as generative AI offers potential productivity gains while also making it easier to conceal effort. A first study examines managerial evaluations and deskilling through experiments involving large language models and human participants. It investigates when managers prioritise tasks that are difficult for AI to replicate, such as mentorship and coaching, and when they delegate the evaluation of their subordinates entirely to AI.

This is a full-time position based in Singapore, working on the Kent Ridge campus with Dr Yeo Shang Long (Centre for Biomedical Ethics, Department of Philosophy), Dr Lee Kwok Hao (Department of Strategy and Policy, NUS Business School) and with another member of the Department of Economics (potentially Prof Lorenz Goette or Prof Jessica Pan). The position will be for one year starting 10 Aug 2026, or as soon as possible.

Duties may include, but are not limited to: 1) Providing programming support for research projects (particularly coding surveys and potentially LLM apps), 2) Conducting statistical and econometric analyses of human and machine behaviour, 3) Conducting literature reviews (of AI biases and behaviour, of human-AI interaction, of AI and healthcare), 4) Contributing to research manuscripts, reports, and dissemination of findings.

Qualifications

The appointed candidate should:

• Hold at least a bachelor’s degree (ideally in economics, psychology or related disciplines)
• Be able to aid in research administration (compiling information from websites and documents, drafting and compiling research-related documents like ethics approval forms)
• Be able to assist in at least two of the following:

1. Literature review (read, take structured notes of conceptual and empirical papers on generative AI, human-AI interaction, and healthcare and AI).
2. Statistical programming and analysis (conduct and double-check statistical tests ranging from t-tests to regressions, perform power analysis, draft and check preregistration plans)
3. General programming of surveys and LLM apps

Experience with statistics or econometrics is preferred. Familiarity with Python and R is advantageous but not required.

Job skills for statistical and general programming will include:

• Familiarity with empirical methods from either economics or psychology
• General proficiency in at least one programming language (Python preferred)
• Willingness to learn Python or R (training can be arranged)
• Ability to work independently and quickly
• Meticulousness, attention to detail, and a strong sense of responsibility

Jobs skills for literature review will include:

• Familiarity with and interest in issues around generative AI
• Excellent comprehension of new concepts, arguments, and distinctions
• Excellent writing ability, particularly in being concise and accurate
• Ability to work independently and quickly
• Meticulousness, attention to detail, and a strong sense of responsibility

Applicants should send the following by email to Dr. Yeo Shang Long (phiysl@nus.edu.sg), Dr. Lee Kwok Hao (kwokhao@nus.edu.sg), and Prof Lorenz Goette (ecslfg@nus.edu.sg).

1. Cover note introducing themselves (no more than 500 words);
2. A two-page Curriculum Vitae (only including relevant information), containing details of two named referees;
3. A sample of published work and/or code, and their roles in the projects, if applicable.

Face-to-face interviews (including measures for programming proficiency where appropriate) will be conducted for candidates. We are accepting applications until the position is filled.