CU Boulder invites applications for a tenure-track faculty member at the Assistant Professor level specializing in environmental data science. The position is rostered in CIRES, with tenure and teaching responsibilities in the Department of Geography in the College of Arts & Sciences. The new faculty member will play a leadership role within ESIIL (Environmental Data Science Innovation & Inclusion Lab) and advance their own extramurally funded research agenda that aligns with and uses ESIIL capabilities and resources. Click here for the full job ad and to apply. Full consideration date is November 11th.
Environmental research themes that focus on data-intensive exploration of the biosphere will be prioritized, such as AI for Earth Observations; biodiversity and the data revolution; data-driven inquiry on natural hazards and responses of ecosystems/communities; understanding disturbance and system thresholds in landscape ecology; climate and global change impacts, forecasting ecosystem health; data science for adaptation and resilient environmental futures, or other relevant data-driven themes. Advanced analytical and computational approaches could include AI and ML approaches, innovative data harmonization techniques, advanced statistical approaches, robust scientific software, new cloud-compute architectures, etc. The research will also use a broad swath of environmental data types and sources such as remote sensing data, e.g., Uncrewed Aerial Systems (UAS) to airborne collections to satellite sensors; observatory field network data (e.g., NEON, CZNet, LTER); paleo records; synthetic or modeled environmental data; biorepositories; eDNA; citizen science and crowdsourced data; or other relevant environmental/biological data.
There is a three-course teaching load per year associated with this position. Teaching needs are associated with the three-course professional certificate in Earth Analytics that is taught through Earth Lab and Geography. Two courses will be taught in the professional certificate in Earth Analytics (which is taught in a hybrid-mode) and a third course will be taught in Geography on remote sensing applications, geographical information science, physical geography, or advanced analytical approaches using environmental data (determined based on the candidate’s specific expertise).
Minimum academic requirements include a PhD at the time of appointment in geography, ecology, ecosystem science, evolutionary biology, or a related field such as computer science, data science, information science, and/or geographic information science, with a demonstrated research record in environmental science.
We would like the candidate to have a foundation in data science and skills (e.g., experience in distributed computing and workflow management either on HPC, HTC, or Cloud) and a commitment to open science practices. The candidate will also demonstrate a commitment to greater diversity and inclusion in the field of EDS.