Postdoctoral Fellows: Machine Learning and Mathematical Analysis of Spatial Transcriptomics Data
The Single-Cell In Situ Spatial Omics at subcellular Resolution (SCISSOR) team is looking for smart, motivated machine learning and data analytics researchers who can lead or contribute to the development of new imaging-based methods for understanding and diagnosing cancer. The candidate should have strong mathematical intuition and programming skills, and be comfortable working in a highly collaborative multidisciplinary environment that includes biologists, imaging technologists, pathologists and medical oncologists. This will be a unique opportunity to work on new data types and ask new questions in the rapidly growing field of spatial Omics.
SCISSOR is a well-supported multidisciplinary program that creates spatial and non-spatial genomic technologies, applies them to clinical samples, generates massive multimodal datasets and interprets them to infer the molecular drivers of tumor growth and response to immunotherapy and other treatments. We have 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).
- PhD in mathematics, computer science, statistics, engineering, machine learning, signal processing, computer vision, computational genomics, or a related field
- Strong programming skills
- Quantitative intuition, and some mathematical maturity
- Strong publication record
- Strong communication skills
- The ability to work closely with clinicians and experimental biologists