Open Postdoctoral position, faculty mentor Sylvia Plevritis

Important Info

Faculty Sponsor First name:

Sylvia

Faculty Sponsor Last Name:

Plevritis

Stanford Departments and Centers:

Biomedical Data Science

Postdoc Appointment Term:

Initial appointment of no less than one year, with the option to extend two more years contingent on funding, adequate progress, and mutual agreement.

Appointment Start Date:

As soon as possible

Group or Departmental Website:

https://plevritislab.stanford.edu/

https://ccsb.stanford.edu/

How to Submit Application Materials:

Go to: https://plevritislab.stanford.edu/openings-within-plevritis-lab

Does this position pay above the required minimum?:

Yes. The expected base pay range for this position is listed in Pay Range field. 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.

Pay Range:

$76,400 - $78,000

We are seeking a postdoctoral scholar in computational biology to work in the laboratory of Professor Sylvia Plevritis in the Department of Biomedical Data Science at Stanford University. This research opportunity will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including single-cell RNA-seq, spatial transcriptomics and multiplexed immunofluorescence in-situ images, in the study of immune-stromal-cancer interactions related to cancer progression and drug response. The scholar will aim to reveal molecular mechanisms through which the tumor microenvironment impacts tumor invasion, metastatic progression, and drug response, using high-throughput data derived from cellular subpopulations within tumors. The individual will work closely with experimental biologists to translate computationally-derived results into experimentally testable hypotheses and analyze the resulting data from the experiments.

Required Qualifications:

Candidate must have a strong quantitative background, with a PhD in computational biology, bioinformatics or related field including bioengineering, computer science, statistics, or mathematics.
Strong knowledge in bioinformatics, machine learning, statistics and programming skills (R, Python, or MATLAB) are required.
Record of peer-reviewed publications.
Knowledge in one or more of the following areas is desirable: single-cell profiling technologies, spatial omics data, immunology and cancer
systems biology.
Ability to work independently and collaboratively within multidisciplinary teams are essential.
Excellent verbal and written communication skills.

Required Application Materials:

Cover letter describing relevant research experiences, accomplishments, interests, and goals
Curriculum vitae
Contact information of three referees