Programme(s) to which this project applies:
|☑ MPhil/PhD||☒ MRes[Med]||☒ URIS|
In recent years, the rapid development of single-cell and spatial sequencing technologies brings unprecedented opportunities to disentangle the heterogeneity in cell populations, including different immune cells, differentiation trajectory or cancer mutations. However, it remains highly challenging to decipher how a biological system functions and the underlying patterns of the data, not only because of high technical noise but also the high dimensions of the gene or other molecular feature space.
Therefore, this project aims to develop machine learning methods, likely in a form of probabilistic models or deep neural networks, to analyse single-cell genomic data. Specific projects will be determined together between the student and PI. Potential directions include but not limited to:
We are generally looking for candidates with a strong quantitative background in (scientific) computing, and interests in biomedical challenges.
Dr Y Huang, School of Biomedical Sciences
Dr Huang is an Assistant Professor, jointly affiliated with the School of Biomedical Sciences and the Department of Statistics and Actuarial Science at HKU. Prior to joining HKU, he was an EBPOD research fellow at the University of Cambridge and the European Bioinformatics Institute (EMBL-EBI). Dr Huang completed his BEng in Automation from Tsinghua University (2009-2013) and PhD in Informatics (Machine learning and computational biology) from the University of Edinburgh (2014-2017).
For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description. Interested candidates are advised to enclose with your email:
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