Important Info
Faculty Sponsor (Last, First Name):
Shahrokh Esfahani, Mohammad
Stanford Departments and Centers:
Radiation Oncology
Postdoc Appointment Term:
1-year term with possible extension
Appointment Start Date:
August 2024, with the exact date being negotiable
Group or Departmental Website:
How to Submit Application Materials:
Please email application materials the PI (shahrokh@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 Esfahani Lab, located within the Department of Radiation Oncology at Stanford University, is seeking an exceptional individual to join the team and contribute to groundbreaking research in computational oncology. We are dedicated to unravelling the complexities of the human regulome through advanced cell-free DNA profiling and developing cutting-edge computational algorithms and molecular profiling techniques. Our research focuses on early cancer detection, minimal residual disease (MRD) detection, and the multi-omic characterization of various cancer types.
Required Qualifications:
- PhD in related fields such as computational biology, cancer biology, and bioengineering (with considerable computational experience). MDs with prior experience in computational oncology are encouraged to apply too.
- Strong record of publications in peer-reviewed journals
- Skilled in the use of R and/or Python
- Basic understanding of statistical modeling, and machine learning
- Understanding of high-throughput sequencing techniques including whole genome, whole exome, targeted capture, RNA-Seq, scRNA-Seq, ATAC-Seq, scATAC-Seq, etc.
- Strong oral and written skills, and capable of writing and publishing results.
- Contribute to lab meetings and one-on-one discussions.
Desirable Qualifications:
- Familiarity with (NGS related) software tools and packages such as bwa, STAR, bedtools, samtools, Picard, RSEM, GISTIC, GATK, Strelka, Seurat
- Prior experience in analyzing spatial transcriptomic data.
- Prior experience in developing models to analyze temporal data.
- Prior experience in utilizing Bayesian modeling in related applications.
Required Application Materials:
- CV
- Contact information for reference letters (required: 2; desired: 3)
- A two-page summary of two major publications that the applicant led or contributed to