Postdoctoral Associate in Environmental Data Science at Yale School of the Environment
This is an exciting opportunity to join the Yale School of the Environment’s initiative in Environmental Data Science.
The successful candidate will contribute to data intensive research on the environment and individuals’ or society’s relationship to the environment and mentoring in the Yale’s non-degree online environmental data science certificate program, https://environment.yale.edu/certificates/data.
Research (50% time): The successful candidate will spend about half of his/her/their time conducting data intensive research related to environmental or natural resource questions. This can involve applications or methods. Research is expected to be quantitative and data driven. Research on the feedbacks between choices and the environment, natural resources, or nature-related risk or on valuing nature resources or environmental services or natural capital will be given preference. Other topics may be proposed. The expectation is Prof. Eli Fenichel, https://environment.yale.edu/directory/faculty/eli-fenichel, will be the successful candidate’s primary research mentor.
Mentoring (50% time): The successful candidate will spend about half of his/her/their time serving as the lead mentor, under the supervision of Prof Fenichel, for Yale’s online non-degree certificate program in environmental data science. Tasks will include: refreshing content as necessary (most content is already developed), planning and implementing weekly live session mentoring sessions with the with the online learner cohort from September through April, organizing assistant mentors, meeting with online leaners as needed, helping select the learner cohort. Support will be provided by the certificate program’s administrator.
The position is renewable depending on performance and funding.
Qualifications
PhD in relevant discipline, e.g., environmental statistics, environmental economics, ecology, etc. and demonstrated strength in applied data science with environmental applications.
Teaching experience.
Expertise in R and python.
Experience with git and Github.
Ability to conduct independent and collaborative research.
Strong English language writing and communication.
Additional Desirable Qualifications
Teaching experience in data science or statistics, connected to environmental questions.
High performance or cloud computing experience.
Experience with large data sets, including spatial data, administrative data, or consumer data.
Benefits
Yale offers an excellent and exciting interdisciplinary environment. Yale has many exciting initiatives related to data that this post-doc could tap into including an environmental data science initiative, Yale Center for Geospatial Solutions, Data Intensive Social Science Center (DISSC), Computational Research Support staff, many licensed data sets, and more. Furthermore, Dr. Fenichel’s research in this area has help, and is expected to continue to help, shape national and global policy. This position offers a competitive salary and excellent benefits.
To apply
To apply or for more information Eli Fenichel at eli.fenichel@yale.edu.
Applications should be a single pdf containing
a cover letter describing interests and skills as related to the job description,
a one-page research proposal,
a CV,
a link to a public code repository (e.g. Github) or a one-page description of a computational project he/she/they has engaged in outside of a class,
the contact information for three references, and
a copy (or link to) the candidates Job Market Paper if they have one or a link to a paper they have led.
Yale University is an Affirmative Action/Equal Opportunity Employer and welcomes applications from women, persons with disabilities, protected veterans, and members of minority groups.
Source: Shared by Eli Fenichel to the RESECON listserv