PhD Students / Post Doctoral Fellows (Spatial Transcriptomics And Single Cell Oncology Program, Computational Track)
Position Code: SC10337
We are a large, 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 oncologists (lead: Iain Tan), computational biologists (lead: Shyam Prabhakar), 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; Lancet Oncol 2012).
Machine Learning and Mathematical Analysis of Spatial Transcriptomics Data
Successful candidates 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 in the nascent field of spatial transcriptomics.
- 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 (postdoctoral candidates).
- Strong communication skills.
- The ability to work closely with clinicians and experimental biologists.
To apply, please email your CV and names of references to: email@example.com
The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice.
* We regret that only shortlisted candidates will be notified.