Apply through sending an email with all required attachements to: datascience @ stanford . edu - Subject: Post-Doc Fellowship Application.
Applications due 14 December 2020
Stanford Data Science seeks recent Ph.D.s of exceptional promise for postdoctoral fellow positions in interdisciplinary research with expertise in both Data Science AND its application in a domain of scholarship, like physical, earth, life, or social sciences, humanities and the arts, business, law, medicine, education, or engineering.
Data Science Post-Doctoral Fellows work both within and at the boundaries between data science methods and the domains of scholarship that utilize data science to discover and create new knowledge. They will lead independent, original research programs with impact in one or more research domains and in one or more methodological domains (computer science, statistics, applied mathematics, etc).
Ideal candidates will have earned a PhD in either a methods or applied discipline with demonstrated skills and experience in one of the other complementary areas (as examples: a PhD in statistics with applications to physics, or a PhD in biology with extensive use of machine learning). Successful candidates will bring a research agenda that can take advantage of the unique intellectual opportunities afforded by Stanford University, and will have experience in working with researchers across different fields. Their research results will be published in technical reports, open-source software, peer-reviewed journals as well as presented at scientific conferences. Ideal candidates will have experience and interests in building community, teaching and training, and leadership with strong communication skills.
Applicants should expect traveling as a requirement to coordinate research with internal and external collaborators and sponsors.
- Recent PhD with experience in a complementary field(s).
- Excellent experience in their PhD discipline
- Excellent knowledge of advanced software engineering, computer science and/or statistics
- Demonstrated commitment to reproducibility and open research through existing public release of research data and software code
- Excellent verbal and written communication and presentation skills necessary to author technical and scientific reports, publications, invited papers, and to deliver scientific presentations, seminars, meetings and/or teaching lectures.
- Experience collaborating effectively with a team of scientists of diverse backgrounds.
- Experience in developing curriculum and teaching.
- Experience developing open-source research software used by a community beyond their lab.
- Experience building inclusive communities of practice around data science that are diverse and equitable for all.
- Applicants should submit their
- (1) curriculum vitae,
- (2) a publication/software list, and
- (3) a two-page letter of intent detailing a proposed research plan. The proposed research plan should include information about both advancing data science and its application in a domain of scholarship. Please also include the names of potential faculty collaborators (ideally bridging a methods domain and an application domain, e.g. Stats+Bio, CS+politics, etc).
- Applicants are encouraged to discuss their proposed research plan with potential faculty collaborator(s) in preparing their application.
- Applicants should arrange to have two letters of reference submitted to the application email address with the subject line: Reference Letter for <applicant's name>.