Important Info

Faculty Sponsor (Last, First Name): 
Betof Warner, Allison
Other Mentor(s) if Applicable: 
Zina Good
Stanford Departments and Centers: 
Med: Oncology
Postdoc Appointment Term: 
Position for 1 year, extendible based on performance.
Appointment Start Date: 
1/16/2024
How to Submit Application Materials: 

Please submit Required Application Materials to Gernot Kaber (gkaber@stanford.edu).

Does this position pay above the required minimum?: 
No. The expected base pay for this position is the Stanford University required minimum for all postdoctoral scholars appointed through the Office of Postdoctoral Affairs. The FY25 minimum is $73,800.

The Betof Warner Lab at Stanford University School of Medicine is seeking a highly motivated postdoctoral fellow to join our team focused on advancing the understanding and efficacy of tumor-infiltrating lymphocyte (TIL) therapies for melanoma. This position offers a unique opportunity to work at the intersection of cutting-edge cancer immunotherapy and advanced computational biology. 

Position Overview: 

• Full-time postdoctoral position in computational biology/bioinformatics 

• Focus on analyzing multi-omic data from melanoma patients receiving TIL therapies 

• Co-mentorship by Dr. Allison Betof Warner and Dr. Zinaida Good 

Key Responsibilities: 

• Adapt internal computational pipelines to analyze high-dimensional patient datasets, including single-cell sequencing, spatial transcriptomics, clinical, and other relevant correlative data. 

• Integrate and interpret diverse data types to gain insights into TIL therapy outcomes and mechanisms 

• Collaborate with wet-lab researchers and clinicians to translate findings into actionable hypotheses 

• Present findings at lab meetings, conferences, and in peer-reviewed publications 

• Contribute to grant writing and project planning

Required Qualifications: 

• Ph.D. in Computational Biology, Bioinformatics, Computer Science, or a related field 

• Strong programming skills in R and/or Python 

• Experience with analysis of single-cell sequencing data 

• Familiarity with spatial transcriptomics analysis 

• Interest in cancer biology and immunology principles 

• Excellent written and verbal communication skills 

Preferred Qualifications: 

• Experience with machine learning approaches for biological data analysis 

• Knowledge of tumor microenvironment and cancer immunotherapy concepts 

• Track record of peer-reviewed publications in relevant fields 

• Experience with cloud computing, version control systems (e.g., Git), and reproducible research practices

• Ph.D. in Computational Biology, Bioinformatics, Computer Science, or a related field 

• Strong programming skills in R and/or Python 

• Experience with analysis of single-cell sequencing data 

• Familiarity with spatial transcriptomics analysis 

• Interest in cancer biology and immunology principles 

• Excellent written and verbal communication skills 

Required Application Materials: 

• Cover letter detailing your research experience and interest in this position 

• CV including publication list

 

Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.