Research Assistant I/II in the Department of Medicine, School of Clinical Medicine (Ref.: 537115)
We now invite applications for the captioned post.
Duties and Responsibilities
- Perform statistical analysis, data cleaning, manipulation and visualisation
- Conduct literature reviews and contribute to manuscripts, grant applications and reports
- Conduct cell-based antiviral assays and support work in animal models of infectious disease
- Contribute to bioinformatics analysis of sequence data from viral isolates
- Analyse respiratory virus surveillance and clinical data, including electronic medical records-based cohort studies
- Support estimation of disease burden, seasonal patterns and age-specific risk
- Assist with transmission or simulation modelling as required
- Provide general administrative and operational support
- Perform other duties as assigned
The appointee will support two related programmes of work in respiratory virus research:
- Antiviral development and resistance: laboratory characterisation of antiviral candidates and resistance-associated mutations, combining cell-based assays with in vivo models.
- Respiratory virus epidemiology in Hong Kong: analysis of surveillance, clinical and cohort data to characterise respiratory virus infection burden, seasonality and transmission in the local population.
Enquiries about the duties of the post should be directed to Prof John Choi at jmhchoi@hku.hk
Requirements
- Hold a Master’s degree or above, preferably in Epidemiology, Biostatistics, Statistics, Computer Science, Biomedical Science, Computational Biology, or related disciplines
- Demonstrated proficiency in analysing quantitative data using R, Python, Stata or SAS
- Experience in working with large or complex datasets
- Practical wet-laboratory experience, particularly cell culture, virological assays or animal handling
- Strong organisational skills, attention to detail, and the ability to manage concurrent tasks across more than one project
- Excellent written and spoken English
- Ability to work both independently and as part of a team
- Applicants with the following attributes would be highly preferred,
- Experience with respiratory virus surveillance data, time-series or seasonality analysis, or infectious disease burden estimation
- Familiarity with routinely collected health data, including Hospital Authority or comparable electronic medical record-derived datasets
- Experience with whole-genome sequencing data or bioinformatics pipelines
- Familiarity with causal inference methods, including directed acyclic graphs and Mendelian randomisation, or with the evaluation of vaccines and immunoprophylaxis
- Experience with agent-based or compartmental modelling, or machine learning applied to health data
- Working proficiency in Cantonese and written Chinese
- A record of peer-reviewed publications
Applications from candidates who meet most but not all of the preferred attributes are welcomed; and depending on the successful candidate's background, the balance between laboratory and computational duties can be adjusted.
What We Offer
The appointment will commence as soon as possible on a 1-year temporary basis or 2-year fixed-term basis, with the possibility of renewal subject to satisfactory performance and funding availability.
A highly competitive salary commensurate with qualifications and experience will be offered. The appointment on fixed terms will attract a contract-end gratuity and University contribution to a retirement benefits scheme at 10% of basic salary. Other benefits include annual leave, medical benefits, and free access to on-campus gyms and libraries.
How to Apply
The University only accepts online application for the above post. Applicants should apply online and upload an up-to-date C.V., preferably with an academic transcript. Review of applications will start from September 2, 2026 and continue until October 31, 2026, or until the post is filled, whichever is earlier.
The University is an equal opportunities employer and
is committed to equality, ethics, inclusivity, diversity and transparency
