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Postdoctoral Fellow in Single-Cell Data Science

Employer
THE UNIVERSITY OF HONG KONG
Location
Pok Fu Lam, Hong Kong
Closing date
17 Apr 2020

Work type: Full-time
Department: School of Biomedical Sciences (22600)
Categories: Academic-related Staff

Applications are invited for appointment as Post-doctoral Fellow in the School of Biomedical Sciences (Ref: 499720), to commence as soon as possible for two years, with the possibility of renewal.

Applicants should have a Ph.D. degree in a quantitative field, including but not limited to computational biology, bioinformatics, machine learning and statistics. Experience of working with statistical modelling and/or genomic sequencing data analysis is preferred.  Those with less experience but with a strong interest in biology are also encouraged to apply.

The appointee will conduct research in the group of Dr. Yuanhua Huang (https://web.hku.hk/~yuanhua/) under the theme of single-cell data science. The specific projects and aims can be defined together with the Principal Investigator.  Potential directions include statistical modelling of somatic mutations for cancer evolution and integrative analysis of single-cell multi-omics data.

Dr. Huang has rich experience in statistical machine learning and computational biology, particularly in the development and application of Bayesian methods in single cell genomic data analysis.  Additionally, there are excellent collaborators locally and internationally on both generating exciting single cell sequencing data and developing computational methodology.  Enquiries about the post should be sent to Dr. Huang at yuanhua@hku.hk.

A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits.

The University only accepts online application for the above post. Applicants should apply online and upload an up-to-date C.V.  Review of applications will start from March 10, 2020 and continue until April 17, 2020, or until the post is filled, whichever is earlier.

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