School of Nursing
Postdoctoral Fellow
(Ref. 260831005)
[Appointment period: twenty-four months]
Duties
The appointee will assist the project leader in the research project - “AI-driven risk profiling and optimization of control strategies for early cardiovascular-kidney-metabolic syndrome in Chinese and Western populations for precision prevention and intervention”. The project aims at developing and validating artificial intelligence (AI)-driven risk profiling models and identify optimised prevention and intervention strategies for early cardiovascular-kidney-metabolic syndrome across Chinese and Western populations.
The appointee will be required to:
(a) lead and coordinate research activities related to data collection, data management, data harmonisation and quality control across study sites and/or datasets;
(b) develop, validate and interpret AI/machine learning-based risk prediction and risk stratification models for early cardiovascular-kidney-metabolic syndrome;
(c) conduct advanced machine learning analyses, epidemiological modelling and/or causal inference analyses using clinical, behavioural, biomarker, lifestyle and/or population health data;
(d) prepare research reports, manuscripts for publication in peer-reviewed journals, conference abstracts and presentations;
(e) assist in grant applications, ethics submissions, project progress reports preparation and knowledge transfer activities;
(f) supervise and provide guidance to junior research staff, research students and project assistants where appropriate; and
(g) perform any other duties as assigned by the project leader or his/her delegates.
Qualifications
Applicants should have:
(a) a doctoral degree in Epidemiology, Biostatistics, Data Science, Artificial Intelligence, Biomedical Informatics, Public Health, Nursing, Medicine, Health Sciences, Computer Science, Statistics or a related discipline and must have no more than five years of post-qualification experience at the time of application;
(b) a strong research background in one or more of the following areas: cardiovascular disease, kidney disease, diabetes, obesity, metabolic syndrome, cardiovascular-kidney-metabolic syndrome, chronic disease prevention, precision health, epidemiology or population health;
(c) demonstrated experience in quantitative data analysis and/or AI/machine learning modelling, preferably using large-scale cohort, electronic health record, registry, clinical trial or population-based datasets;
(d) proficiency in statistical or programming software such as R, Python, SAS, Stata, SPSS and/or relevant machine learning platforms;
(e) a good publication record and ability to prepare manuscripts independently;
(f) a good command of written and spoken English;
(g) good analytical, organisational, communication and interpersonal skills; and
(h) the ability to work independently as well as collaboratively in a multidisciplinary research team.
Preference will be given to those with (i) proficiency in Chinese, including Cantonese and/or Putonghua; (ii) experience in cross-population comparative studies, international datasets, digital health, implementation science or precision prevention research; and (iii) experience in predictive modelling, risk profiling, survival analysis, longitudinal data analysis, causal inference, model validation or health data harmonisation.
Applicants are invited to contact Prof. Yang Lin at telephone number 2766 6398, fax number 2364 9663 or via email at l.yang@polyu.edu.hk for further information.
Conditions of Service
A highly competitive remuneration package will be offered.
Consideration of applications will commence on 7 September 2026 until the position is filled.
Posting date: 31 August 2026