Job Description
Assistant Professor (Presidential Young Professor) in Computational Communication
The Department of Communications and New Media at the National University of Singapore invites applications for tenure-track positions at the Assistant Professor (Presidential Young Professor) rank, with expertise in one or more of the following areas: media studies, visual communication, science communication, cultural studies, and computational communication. We are especially interested in candidates with a focus on Asia-centred and artificial intelligence research.
The NUS Presidential Young Professorship and NUS Presidential Fellowship Awards aim to attract, support, and empower the brightest and most promising young academics at the early stages of their careers, helping them develop into world-class scholars. As an academic at NUS, you will have the opportunity to work alongside a diverse and dynamic community of scholars and students, pursue excellence and innovation, and make a positive societal impact. Successful PYP candidates are awarded a start-up grant package of either SGD 250,000 or SGD 750,000, depending on the demands of their research programme. They are also granted an additional SGD 250,000 for discretionary spending.
The National University of Singapore (NUS) is a leading research-intensive university that is consistently ranked among the world’s top universities. Remuneration is competitive and includes medical, housing, relocation and other benefits. Significant research start-up funding is available.
Singapore is a modern, English-speaking city state that is connected to the world via global commerce, finance and transport networks with a stable climate year round and a cosmopolitan mix of cultures and languages.
Qualifications
PhD in Communication or closely related discipline by date of appointment, with a specialisation in methods for digital data collection, analytics, and/or visualisation. Evidence of excellence in computational communication research and teaching. Candidates should demonstrate the ability to conduct research that applies data science in communication contexts such as data journalism and data-driven strategic communication for health, marketing, or politics, and considers the implications of data-driven methods for building and validating communication and/or information theory. Candidates must have experience with computational techniques using R or Python and the ability to teach courses in computational communication, social media research, network analysis, and/or time series analysis.
How to Apply (Closing Date: 30 September 2025)
Interested applicants are invited to apply directly through our NUS Career Portal. Application dossiers should include:
- A brief cover letter explaining why the position at NUS is of interest and discussing the ambition of your research programme
- A comprehensive CV with a complete list of publications, awards, grants, and courses taught
- A research statement with a three-year research plan
- Two signature publications with the applicant as the lead author
- A list of six references (names, contact details, and the applicant’s relationship to them) should be provided. These references must include the applicant’s PhD or post-doctoral advisor/supervisor. Shortlisted candidates must arrange for the letters of reference to be submitted prior to the campus visits. If the applicant is two or more years post-PhD, the referees should not be limited to supervisors. Referees should compare the applicant’s research performance to individuals at equivalent and top institutions or programmes. Referees are required to send their letters directly to cnmcareer@nus.edu.sg.
We regret that only shortlisted candidates will be notified. For any questions, please email cnmcareer@nus.edu.sg.
To ensure full consideration, applications must be received by 30 September 2025 (Singapore Time 11:59PM). Only shortlisted candidates will be notified.
More Information
Location: Kent Ridge Campus
Organization: Arts & Social Sciences
Department : Communications And New Media
Employee Referral Eligible: No
Job requisition ID : 30232
