Jobs UNIVERSITY OF COLORADO BOULDER

Machine Learning for the Environment at CU Boulder CS: This postdoctoral fellow will be advised by Professors Esther Rolf and Claire Monteleoni in the Computer Science Department. Professor Rolf’s lab specializes in geospatial and statistical machine learning. Her research group develops computationally efficient models and algorithms tailored to geospatial machine learning with remotely sensed data and analyzes how the representivity of training and pre-training datasets influences the accuracy and equity of machine learning systems across global regions and populations. Professor Monteleoni is the AI/ML Lead for the Environmental Data Science Innovation and Inclusion Lab (ESIIL). Her research group, Climate & Machine Learning Boulder (CLIMB), develops machine learning approaches to confronting climate change at a variety of time-scales.

About ESIIL: The postdoctoral fellow will also have an advisor in the Environmental Data Science Innovation & Inclusion Lab (ESIIL), an NSF-funded data synthesis center led by the University of Colorado Boulder in collaboration with NSF’s CyVerse at the University of Arizona and the University of Oslo. ESIIL’s mission is to empower an inclusive and diverse community to accelerate open Environmental Data Science. ESIIL is part of the Cooperative Institute for Research in Environmental Sciences (CIRES) at CU Boulder and enables a global community of environmental data scientists to leverage the wealth of environmental data and emerging analytics to develop science-based solutions to solve pressing challenges in environmental sciences. ESIIL’s research community generates discoveries and novel approaches through 1) cutting-edge team science, 2) innovative tools and collaborative cyberinfrastructures, 3) data-science education and training, and 4) building inclusive participation and diverse groups. These activities advance the frontier of environmental data science, a rapidly evolving discipline bridging the computational, biological, environmental, and social sciences. ESIIL holds inclusion as a core principle and method for diversifying EDS at a time when society needs all perspectives, and science needs to serve all. ESIIL’s vision is grounded in the conviction that innovation and breakthroughs in EDS will be precipitated by a diverse, collaborative, curious, and inclusive research community empowered by open data and infrastructure, cross-sector and community partnerships, team science, and engaged learning.

The “ML for the Environment” Post-Doctoral Research Scholar is expected to produce innovative machine learning research while also representing and encouraging a diverse and interdisciplinary community of ML/AI researchers in the rapidly developing field of EDS.

• Develop and implement a research project that advances machine learning methods, analysis, and/or tools for environmental data science. Examples of relevant projects may include (but are certainly not limited to): developing new machine learning models and methods for efficient, robust, and equitable environmental data science, establishing new benchmark datasets that address gaps in machine learning benchmarks for environmental data science, working with community partners to develop an application-driven machine learning system centered around on-the-ground needs.
• Work collaboratively with both members of the Computer Science Department and the ESIIL network. Foster bridges between these communities, for example through collaborative research projects, working group(s), hackathon teams, or trainings for the ESIIL community.
• Contribute to the open, reproducible science and education objectives of ESIIL, which include creating and publishing well-documented datasets and codes, FAIR-compliant code workflows, or other outputs that will serve the larger ML and EDS communities.
• Effectively share research results with colleagues and the broader science community through peer-reviewed publications, presentations, and other science products.
• Interact with a cohort of ESIIL postdocs working on ML/AI applied to various environmental topics. Actively participate in the CS and ESIIL communities and help to promote greater diversity, equity, and inclusion in open environmental data science.

• The anticipated start date is flexible, but is expected to be on or before August 1st, 2025. The selected candidate must have a doctoral degree conferred before the start date of the appointment. This is a one-year position with possibility to renew for a second year dependent on performance.
• Location: The Post-Doc will work in person with offices in the Engineering Center and in ESIIL's main office on the CU Boulder campus in Boulder, CO. A hybrid work modality may be a possibility, but will follow current CIRES, Computer Science, and university guidelines and policies. This is not a fully remote position. The in-person cohort experience and engagement with the Computer Science and ESIIL communities is a key aspect of this position.

• Salary range: $75,000 - $80,000
• CIRES and the University of Colorado Boulder offer a robust training curriculum, opportunities for professional development and a Mentorship Program.
• Boulder is a vibrant community with access to mountain parks, dog parks, miles of trails, rivers, lakes, cafes, restaurants, boutiques, theaters, museums, and sports venues. Boulder was recently ranked as one of the top places to live in the U.S. by U.S. News.
• As an employee at CU Boulder, you will have free access to the regional public transit system, an outstanding network of buses, and light rail systems that service Boulder and connect to Denver, the Denver airport, and surrounding communities.

• Ph.D. in computer science, or data science field (including but not limited to Computer Science, Data Science, Computer Engineering, Information Science, Math, Economics).

• Knowledge of python programming language and machine learning packages (e.g., Python, PyTorch).
• Commitment to Open Science (i.e., available and findable data, reproducible and transferable codes to run on public cyberinfrastructure and open publications) and Open Education.
• Effective project management skills (e.g., self-directed, independent).
• Collaborative spirit and comfort working in scientifically and culturally diverse and inclusive teams.
• Willingness to collaborate and contribute to both the Computer Science and ESIIL research communities (e.g. through working groups, hackathons, and/or summits.)
• Willingness to promote diversity, equity, and inclusion in the ESIIL and Computer Science communities.

We invite applicants to apply even if they do not have the preferred skills and experience outlined in this section. If you meet the minimum qualifications and have passion for the work, you are encouraged to apply. We encourage on-the-job training for any additional skills or knowledge that become relevant to the position. We encourage applicants to highlight their relevant experience or passion with any of the following:

• Experience publishing in core machine learning venues (e.g. ICML/Neurips/ICLR/JMLR, etc, interdisciplinary workshops).
• Experience and/or interest in interdisciplinary, collaborative and inclusive team science.
• Experience with open-source tool development and publication.
• Experience and/or interest in multi-source, big data dataset curation and publication.
• Experience and/or interest in participating in workshops with community partners.