Machine Learning and Mathematical Analysis of Spatial Transcriptomics Data

Genome Institut of Singapore (GIS/A*Star)
Computational & Systems Biology
Singapore Singapur Singapur


The Genome Institute of Singapore (GIS) is an institute of the Agency for Science, Technology and Research (A*STAR). It has a global vision that seeks to use genomic sciences to achieve extraordinary improvements in human health and public prosperity. GIS is dedicated to creating a social culture that is focused on excellence, knowledge sharing, individual initiative and coordinated action. We have focused on recruiting and rewarding excellent people and establishing systems that will sustain this culture of excellence. Our scientists come from all five continents, from industrial and academic positions and from diverse disciplines. This diversity is unified in the pursuit of science for public health and prosperity.

Single-Cell In Situ Spatial Omics at subcellular Resolution (SCISSOR) is a well-supported multidisciplinary program that aims to introduce new paradigms for cancer biology and diagnostics, using spatial and non-spatial omics technologies. Our team comprises computational biologists (lead: Shyam Prabhakar), oncologists (lead: Iain Tan), biotechnologists (lead: Kok Hao Chen), and pathologists (lead: Tony Lim) with a track record of combining cutting-edge computational and experimental approaches to infer disease mechanisms and develop clinical applications (Chen et al., Science 2015; Li et al., Nat Genet 2017; Sun et al., Cell 2016; Fukawa et al., Nat Med 2016; del Rosario et al., Nat Methods 2015; Kumar et al., Nat Biotechnol 2013; Ku et al., Lancet Oncol 2012).

We are looking for bright, motivated individuals who are interested in working on cutting-edge research projects that leverage single cell and spatial omics. Our interdisciplinary team combines experimental biology, technology development and computational biology to address major questions in cancer and neurobiology.
PhD Students/Postdoctoral Fellows: Machine Learning and Mathematical Analysis of Spatial Transcriptomics Data

The successful candidate will develop and apply algorithms for the analysis of large-scale cancer data. This will be a unique opportunity to lead computational analysis of new types of data spanning single-cell epigenomics, single cell RNA-seq and 3D chromatin organization in the nascent field of spatial transcriptomics.

Candidates with relevant undergraduate/Master’s degrees can apply for PhD student positions.


• Strong programming skills
• Expertise in mathematics, computer science, statistics, engineering, machine learning, signal processing, computational genomics, or a related field
• General quantitative intuition
• Strong publication record
• Strong communication skills
• The ability to work closely with clinicians and experimental biologists

Start date

As soon as possible

How to Apply

To apply, please email your CV and names of references to:,