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RILE Distinguished Visiting Professor - Race, Inequality, and Language in Education Program, AY 20-21

Overview

The Graduate School of Education at Stanford University seeks applications for the Race, Inequality, and Language in Education (RILE) Distinguished Visiting Professor Program for the 2020-2021 academic year. The School will select one faculty member to be in residence at Stanford University to join the GSE’s RILE program and be part of our collaborative and engaging community of students, staff and faculty.

Requirements

The Visiting Professor will attend weekly RILE meetings with students, RILE faculty meetings, the RILE colloquia series, give one talk per quarter, and hold 4-5 hours of office hours with students per week. Additionally, the Visiting Professor will offer a series of workshops or a course.

The ideal candidate is committed to linking scholarship and practice in topics related to RILE, interested in working with students and faculty in related areas, and on partial or full sabbatical or leave from his/her/their home institution. The Visiting Professor will use the time at Stanford to further the objectives of his/her/their research that are aligned with the RILE program, explore critical content and innovative methodologies, engage deeply with the community, and collaborate on work in a context that is both receptive and supportive.

Terms

The Visiting Professor will be in residence from September 20, 2020 through June 20, 2021 (observing Stanford’s breaks and holidays). The GSE will provide up to 50% of the faculty salary (depending on the amount of sabbatical/leave offered by the home institution) and a research account of $3,000. The GSE has also identified a range of temporary housing options and will work with the finalist on a monthly housing allowance to support their needs.

Interested applicants should submit the following by December 1, 2019 by email to kpearman@stanford.edu for review of the selection committee and the Dean:

  • Current CV
  • Two letters of reference
  • A 2-3 page description of the work intended for the period of the visit, including how and why the close work with GSE students and faculty is a priority
  • A list of any anticipated conflicts with the published terms and dates of the appointment

Position Flyer

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Faculty Position in Adolescent Development

The Stanford Graduate School of Education is seeking applicants for a tenure-track assistant professor position.  Adolescence is a period of uniquely significant change that has effects on behavior, socialization, and society.  We seek applicants who have broad research and teaching interests in youth and adolescence. Foci of research may include, but are not limited to, social, personality, and/or cognitive development, cultural perspectives, and the study of diverse adolescent populations in and out of schools.  We especially welcome applications from researchers who consider implications for educational practice, who look at change over time, and who can interact with colleagues across multiple fields of inquiry. The successful candidate will teach courses in child and adolescent development at the Master’s and Doctoral levels.  

The successful candidate will contribute to teaching and advising doctoral and masters students in the GSE.  

Applicants are required to provide:

  • a cover letter describing their research agenda and teaching experience
  • curriculum vitae
  • three scholarly publications
  • three letters of reference.

This search is chaired by Professor Bill Damon.

All application materials must be submitted online.  Please submit your application on Interfolio: https://apply.interfolio.com/66881

Application deadline is November 15th, 2019.

Questions pertaining to this position may be directed to Tanya Chamberlain, Faculty Affairs Officer, tanyas@stanford.edu.

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. Stanford also welcomes applications from others who would bring additional dimensions to the University’s research, teaching and clinical missions.

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Faculty Position in Educational Data Science

The Stanford University Graduate School of Education seeks an open-rank, tenure-track faculty member to contribute to a new program in educational data science. We seek candidates from a variety of academic disciplines whose research embodies the growing field of educational data science. We invite applications from candidates who specialize in learning analytics, machine learning, big data and novel uses of new computational methods, such as network analysis and natural language processing techniques. A strong preference will be for applicants who use state of the art methodologies to drive discovery or application in specific educational domains.

The successful candidate will contribute to teaching and advising doctoral and masters students in the GSE.  

Applicants are required to provide:

  1. a cover letter describing their research agenda and teaching experience
  2. curriculum vitae
  3. three scholarly publications

Applicants for Assistant rank positions should submit three letters of reference.

Applicants for Associate and Full Professor ranks should submit a list of three names of references (complete with addresses and phone numbers). We will request letters of recommendation for a short list of finalists only.

This search is chaired by Professor Dan McFarland.

All application materials must be submitted online. 

Application review will begin on September 30, 2019.

The Stanford University Graduate School of Education seeks an open-rank, tenure-track faculty member to contribute to a new program in educational data science. We seek candidates from a variety of academic disciplines whose research embodies the growing field of educational data science. We invite applications from candidates who specialize in learning analytics, machine learning, big data and novel uses of new computational methods, such as network analysis and natural language processing techniques. A strong preference will be for applicants who use state of the art methodologies to drive discovery or application in specific educational domains.

https://apply.interfolio.com/66742

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