Machine Learning and Environment Postdoctoral Fellow

About the Role

Key Responsibilities

• Develop and implement a research project that advances machine learning methods, analysis, and/or tools for environmental data science, including developing new models, establishing benchmark datasets, or working with community partners on application-driven ML systems.
• Work collaboratively with members of the Computer Science Department and the ESIIL network, fostering bridges between these communities through research projects, working groups, hackathons, or trainings.
• Contribute to open, reproducible science and education objectives of ESIIL by creating and publishing well-documented datasets and codes, FAIR-compliant workflows, or other outputs for the ML and EDS communities.
• Share research results with colleagues and the broader science community via peer-reviewed publications, presentations, and other science products.
• Interact with a cohort of ESIIL postdocs working on ML/AI applied to environmental topics and actively participate in the CS and ESIIL communities to promote a collaborative, open environmental data science network.

Requirements

• Ph.D. in computer science, data science, or related fields such as computer engineering, information science, math, or economics.
• Knowledge of Python programming language and machine learning packages (e.g., Python, PyTorch).
• Commitment to Open Science, including available and findable data, reproducible codes, and open publications.
• Effective project management skills, including being self-directed and independent.
• Collaborative spirit and desire to work in interdisciplinary teams.
• Willingness to contribute to both the Computer Science and ESIIL research communities through activities like working groups, hackathons, or summits.

Nice to Have

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

Qualifications

• Doctoral degree (Ph.D.) conferred before the start date of Fall 2025 or earlier.

Benefits & Perks

• Salary range: $75,000 - $80,000.
• Access to CU Boulder’s training curriculum, professional development, and mentorship programs.
• Free access to regional public transit system, including buses and light rail.
• Comprehensive benefits including medical, dental, retirement, paid time off, tuition benefits, and ECO Pass.