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Stanford Medicine
Palo Alto, CA, United States
27 days ago

Description

Stanford University

Department of Radiation Oncology

The Division of Radiation and Cancer Biology in the Department of Radiation Oncology at Stanford University seeks a Ph.D. to join the Department as an Assistant Professor in the Non-Tenure Line (Research).

We seek applicants committed to developing an academic program focused on computational oncology in areas relevant to the field of radiation therapy (e.g. radiation biology, treatment response prediction, cancer immunology, cancer detection, etc.). Applicants must have completed a Ph.D. and postdoc in the area of computational oncology. Applicants should also have expertise in development and application of machine learning/artificial intelligence approaches for translational cancer research and demonstrated prior substantial scholarly contributions in these areas.

Applicants must have an interest in leading an independent research program and have the desire to closely collaborate with other investigators in the Division and Department. The successful applicant will spend the majority of time on their independent research program and approximately 25% on collaborating with members of the Division and Department to support computational biology and bioinformatics needs. The ideal candidate will have potential trajectory and/or demonstrated breadth of preparation, as exemplified by significant research experience resulting in high impact publications in computational oncology. A successful candidate will be expected to obtain substantial grant funding (e.g., NIH award(s)).

The major criterion for appointment for faculty in the Non-Tenure Line (Research) is evidence of high-level performance as a researcher for whose special knowledge a programmatic need exists. Individuals appointed as Assistant Professors in the Research Line will have completed one or two years of postdoctoral research experience and, where applicable, will have completed housestaff training. Their accomplishments during graduate and postgraduate training should already have stamped them as creative and promising investigators. There should be evidence of the ability to obtain external funding as well as the promise of outstanding performance as a supervisor of graduate students. Appointment is based on evidence of (or the promise of) high-level performance in research, and (if applicable) teaching and clinical care.

The activities of the Department are diverse and include a strong basic science and translational research programs. The Department is tightly integrated into the medical school and university and leads a Radiation Biology research program within the Stanford Cancer Institute, a National Cancer Institute Designated Comprehensive Cancer Center.

Consideration of candidates will begin immediately. Application will be accepted until the position is filled.

Interested applicants should submit through the Stanford Faculty Positions site a curriculum vitae, a brief statement of research, and the names of three references, addressed to Dr. Ruijiang Li.

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



Requirements

Applicants must have completed a Ph.D. and postdoc in the area of computational oncology. Applicants should also have expertise in development and application of machine learning/artificial intelligence approaches for translational cancer research and demonstrated prior substantial scholarly contributions in these areas.

Job Information

  • Job ID: 63560575
  • Location:
    Palo Alto, California, United States
  • Position Title: Assistant Professor, Computational Oncology (Non Tenure Line-Research in Radiation Oncology)
  • Company Name For Job: Stanford Medicine
  • Industry: Not Specialty-Specific
  • Job Function: Professor/Assistant Prof
  • Setting: Academic Institution
  • Job Type: Full Time Regular

Please refer to the company's website or job descriptions to learn more about them.

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