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Post-doctoral Fellow in Machine Learning and Applied Remote Sensing

Work type: Full-time
Department: Department of Earth Sciences (25300)
Categories: Academic-related Staff

Applications are invited for appointment as Post-doctoral Fellow in Machine Learning and Applied Remote Sensing in the Department of Earth Sciences (Ref: 508203) (to commence as soon as possible for one year, with the possibility for renewal).

Applicants should possess a Ph.D. degree in Remote Sensing, Computational Mathematics, Geoinformatics or related disciplines. Experience in remote sensing image processing and machine learning is essential and experience in applications on surface composition, geology or planetary science would be desirable. Ideally, the applicants should also have a research track record including publications in peer-reviewed journals, and demonstrated interpersonal skills.

The appointee will work jointly with the Research Divisions of Earth and Planetary Science and Mathematics and Statistics.  He/She will be supervised primarily by Dr. Joe MICHALSKI (www.clays.space), and work very closely with co-supervisor Chair Professor Michael Kwok Po NG (https://hkumath.hku.hk/~mng/). He/She will contribute to the supervision of postgraduate students (M.Phil. and Ph.D.), develop teaching skills and participate in grant-writing to secure additional funding if so desired.  Opportunities will also be given to participate in international conferences and workshops, mentoring, training, and other forms of professional development.

A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits.  At current rates, salaries tax does not exceed 15% of gross income.

The University only accepts online application for the above post.   Applicants should apply online and upload an up-to-date C.V. and a cover letter. Applicants should also arrange two reference letters to be sent directly by the referees to Dr. Joe Michalski at jmichal@hku.hk.  All application materials must be in PDF format only.   Review of applications will commence as soon as possible and continue until September 28, 2021, or until the post is filled, whichever is earlier.

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