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Experience :

0 years

Salary :

$71,650

USA

Computational oncology research is led by the Esfahani Lab at Stanford University's Department of Radiation Oncology. Our goal is to utilize cutting-edge cell-free DNA profiling tools to decipher the intricate workings of the human regulome. In order to improve minimal residual disease (MRD) identification, multi-omic characterisation across different cancer types, and early cancer detection, we concentrate on creating state-of-the-art computational algorithms and molecular profiling techniques.

Position:

Postdoctoral Researcher in Computational Oncology

Last Date:

Not specified. Applicants are encouraged to apply at the earliest.

Education:

PhD in computational biology, cancer biology, bioengineering, or related fields

Job Location :

USA

Job Description

We are seeking a highly motivated Postdoctoral Researcher to join our team and contribute to groundbreaking research in computational oncology. The successful candidate will participate in lab meetings and discussions, collaborate on various research projects, and play a key role in advancing our understanding of cancer biology.

Responsibilities:

  • Conduct research in computational oncology with a focus on early cancer detection, MRD detection, and multi-omic characterization.
  • Develop and implement cutting-edge computational algorithms and molecular profiling techniques.
  • Analyze high-throughput sequencing data, including whole genome, whole exome, RNA-Seq, scRNA-Seq, and ATAC-Seq.
  • Contribute to the development of statistical models and machine learning algorithms.
  • Collaborate with interdisciplinary teams to integrate spatial transcriptomic data and analyze temporal data.
  • Present research findings at conferences and publish results in peer-reviewed journals.

Qualifications:

  • PhD in computational biology, cancer biology, bioengineering, or related fields (MDs with computational oncology experience are encouraged to apply).
  • Strong publication record in peer-reviewed journals.
  • Proficiency in R and/or Python.
  • Basic understanding of statistical modeling and machine learning.
  • Familiarity with high-throughput sequencing techniques and NGS-related software tools.
  • Excellent oral and written communication skills.
  • Ability to contribute to lab meetings and discussions.

Desirable Qualifications:

  • Experience in analyzing spatial transcriptomic data.
  • Prior experience in developing models for analyzing temporal data.
  • Knowledge of Bayesian modeling in related applications.

How to Apply:

Interested candidates should email application materials, including CV, contact information for reference letters, and a two-page summary of two major publications led or contributed to, to the Principal Investigator (PI) at shahrokh@stanford.edu.

Deadline: Not specified. Applicants are encouraged to apply at the earliest.

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