Important Info
If interested, please email andrewg AT stanford.edu 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 position. This serves as your ‘cover letter.’
The Gentles lab at Stanford University has postdoc positions available for several projects in analyzing genomic and proteomic data in cancer using systems biology approaches. Our works spans multiple cancer types, using a combination of computational and experimental methods to address questions in tumor biology with a particular interest in the content and organization of the tumor microenvironment, and its influence on patient outcomes and therapy response. As one 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 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 in an iterative process.
Our group members have diverse backgrounds including cancer biology, computer science, and physics. We are located within the Department of Pathology, and have close affiliations with the Departments of Medicine (Biomedical Informatics) and Biomedical Data Sciences. Stanford has state of the art facilities including cores for genomic and proteomic data generation, as well as high performance computing environments.
Background
We are seeking candidates who love doing science and have high problem-solving analysis skills using quantitative methods. A strong interest in the biological questions is critical, as well as motivation to learn new areas of biology and computational methods as needed.
Helping you achieve your career goals is an important part of this position. This includes developing a mentoring plan that assesses the needs and goals of each postdoctoral researcher based on background, interests and needs of each trainee, and identifying activities needed to reach them. Stanford has multiple resources supporting career development such as workshops on preparing K99 awards and other fellowships.
Essential
• PhD in related field such as computational biology, cancer biology (with considerable computational experience). 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 including deep learning
• Analysis of genomics and proteomics data, particular single cell such as scRNA-seq, CyTOF; and spatial technologies such as CODX, MIBI, spatial transcriptomics
• 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
Desirable
• Experience with Bayesian modeling, in particular probabilistic graphical models
• Causal network inference
• Mentoring skills and desire to work with students
• Building community resources using frameworks such as R/Shiny
• Contact details for 3 referees familiar with your work
• A copy of your CV
