Lingnan University is one of the eight publicly funded institutions in the Hong Kong Special Administrative Region (HKSAR) of the People’s Republic of China (PRC) and has the longest established tradition among the local institutions of higher education. It is widely recognised for providing quality education with a focus on whole-person development and conducting high-impact research for a better world. Moving forward, Lingnan University is well positioned to take lead as a comprehensive university in arts and sciences in the digital era, with impactful research and innovations.

Lingnan University offers undergraduate, taught postgraduate, and research postgraduate programmes in the Faculties of Arts, Business, Social Sciences, and the Schools of Data Science, Graduate Studies and Interdisciplinary Studies. To foster interdisciplinary collaboration and scientific progress, Lingnan University established the Lingnan University Institute for Advanced Study (LUIAS), attracting distinguished scholars from around the world to collaborate with its faculty and students. With traditional strengths in arts, business, social sciences, and interdisciplinary studies, the University aims to equip students with practical knowledge and critical thinking skills to thrive in the future. Subsequent to the establishment of the School of Data Science and LUIAS, Lingnan University is transforming into a hub for global leaders to develop and promote human-centric technology and social policies. Further information about Lingnan University is available at https://www.ln.edu.hk/.

Applications are now invited for the following post:

Postdoctoral Fellow
Division of Industrial Data Science
(Post Ref.: 26/112)

The main responsibilities of this post are to (i) conduct research and evaluation studies; (ii) collect and analyse data; (iii) participate in grant proposals and execution; (iv) participate in and help building academy-industry research; (v) advise several graduate students; (vi) draft manuscripts and write reports; (vii) assist in different research activities and handle administrative work and perform any other duties as assigned by the Person-in-charge or her delegates. The appointees are expected to submit applications for RGC grants, other local/overseas competitive grants and/or fellowship schemes.

General Requirements

Candidates should have (i) a PhD degree in imaging science or related field or an equivalent qualification; (ii) demonstrate research skills in imaging science and machine learning, particularly on image reverse engineering, fake image and video detection, statistical detection models and mathematical models of image processing; (iii) demonstrate experience in academy-industry projects; (iv) interest in publishing high-quality journal papers in the field; (v) demonstrated leadership in research projects; and (vi) fluency of written and spoken English required. Candidates should have obtained / been conferred a PhD degree or an equivalent qualification for no more than three years prior to the commencement of the appointment.

Appointment

The conditions of appointment will be competitive. The remuneration will be commensurate with qualifications and experience. Fringe benefits include annual leave, medical and dental benefits, mandatory provident fund, gratuity and incoming passage and baggage allowance for the eligible appointee. Appointment will normally be made on a fixed-term contract of up to three years.

Application Procedure (online application only)

Please click "Apply Now" to submit your application. Applicants shall provide names and contact information of at least three referees to whom applicants’ consent has been given for their providing references. Personal data collected will be used for recruitment purposes only.

We are an equal opportunities employer. Review of applications will continue until the post is filled. Qualified candidates are advised to submit their applications early for consideration.

The University reserves the right not to make an appointment for the post advertised, or to fill the post by invitation or by search. We regret that only shortlisted candidates will be notified.