Applications closed on Dec 14, 2020
Next round will open in September 2021
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 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.
Appointments will be initially for one year, with an expectation of renewal for a second year on satisfactory performance. Fellowships have a competitive salary and benefits, with funds to support research and travel. There is flexibility about the start date, September 1, 2021 is expected.
Recent PhD (graduation by 1 September 2021) 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.
As soon as possible
Required Application Materials
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 below email with the subject line: Reference Letter for <applicant's name>.
Applications closed on Dec 14, 2020 - Next round will open in September 2021.
Stanford University is an affirmative action and equal opportunity employer, committed to increasing the diversity of its workforce. It welcomes applications from women, members of minority groups, veterans, persons with disabilities, and others who would bring additional dimensions to the university's research and teaching mission.