Now accepting applications for working groups!
In line with our mission to foster collaboration across a broad range of disciplines related to environmental data science, the Environmental Data Science Innovation & Inclusion Lab is now accepting applications for working groups. These groups will play a pivotal role in promoting the integrative approach ESIIL champions, pooling knowledge and expertise from various disciplines to tackle environmental issues with a data-driven perspective. Please read the Request for Proposals (RFP) for more information.
What are working groups?
Working groups are self-organized research teams focused on well-defined scientific questions that advance environmental data science and require insights from a diverse group of researchers and other stakeholders. A single working group may have up to 15 participants and a quorum (50% or more) shall meet in person up to 2 times over a 2 year period, with each meeting lasting between 3 and 5 days.
Application Process
Please download this folder containing the three templates required in the proposal & follow the workflow as listed in the RFP.
Important Dates
Proposals must be submitted in PDF format to esiil@colorado.edu by November 1, 2023. Funding decisions will be announced by early 2024, with anticipated start dates of Working Groups as early as February 2024.
Information Sessions
Would you like to learn more about our working groups or do you have questions about the application process? Join us at one of our information sessions!
During each information session we will provide an overview of ESIIL's working groups and answer your questions.
- Info Session 1: October 2nd, 1-2 pm MST
- Info Session 2: October 10th, 1-2 pm MST
Click here to sign up and share your questions with us ahead of time.
What’s the proposal review process?
All proposals will be reviewed based on the criteria outlined in the Request for Proposals document and project feasibility.
We look forward to receiving your proposals and working with you to advance environmental data science.