Postdoc positions in computational cancer biology

Stanford University
Pathology/Medicine/Biomedical Data Science
United States CA Stanford


The Gentles lab has postdoc positions for several projects in analyzing genomic and proteomic data in cancer using systems biology approaches. As an example, we are interested in understanding immunotherapy response in a rare cancer type called clear cell ovarian cancer. We are generating scRNA-seq, CODEX, and other data on clinical samples with known therapy responses. The aim is to use network modeling, including Bayesian approaches, to identify critical nodes or edges that can be targeted to modulate relevant pathways in mouse models, and feed these results back into the modeling.

The expected base pay range for this position is $68,283 - $78,000. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the qualifications of the selected candidate, budget availability, and internal equity.


• PhD in related field such as computational biology, cancer biology (with considerable computational experience), etc. Other quantitative science backgrounds including data science will also be considered.
• Strong record of publications in peer-reviewed journals
• Skilled in the use of R and/or Python
• Familiarity with open source software
• Training in probability, statistics, machine learning; preferably applied to genomics and proteomics data
• Ability to learn and incorporate new methods, and develop and modify them as needed
• Strong oral and written skills, and capable of writing and publishing results
• Ability to work independently in the context of collaborations
• Contribute to lab meetings and one-on-one discussions
• Experience with Bayesian modeling, in particular probabilistic graphical models
• Mentoring skills and desire to work with students
• Background in analysis of single cell data such as scRNA-seq and knowledge of challenges of such data types
• Understanding and experience in analyzing spatial biology from platforms such as CODEX, MIBI, and spatial transcriptomics
• Building community resources using frameworks such as R/Shiny

Start date

As soon as possible

How to Apply

If interested please email with:

• A few sentences about who you are, where you are currently studying / what you are currently working on, and why you are interested in this lab. Briefly, what are your current short/long term career goals? This serves as your ‘cover letter.’
• A copy of your CV (with the filename as [your last name]_[your first name]_CV.pdf)
• Contact details for 3 referees familiar with your work


Andrew Gentles