Staff Position

The Yale Center for Geospatial Solutions (YCGS) is hiring a Lead, Geospatial Artificial Intelligence and Machine Learning to help advance the use of GeoAI and emerging technologies in research across Yale.

This position will support and expand applications of artificial intelligence and machine learning within geospatial research, including areas such as remote sensing, computer vision, geospatial foundation models, generative AI, and LLM-enabled research workflows. The role will collaborate with researchers across disciplines and contribute to building YCGS’s growing GeoAI capabilities.

Location: New Haven, Connecticut
Work arrangement: Hybrid
Position: Full-time

We encourage candidates with experience at the intersection of geospatial science, artificial intelligence, machine learning, and interdisciplinary research to apply.

Learn more and apply:
https://careers.yale.edu/us/en/job/YUCYUHUS138060WDEXTERNALENUS/Lead-Geospatial-Artificial-Intelligence-and-Machine-Learning

Student Job Opportunities

(Last Updated: September 15, 2026)

Student workers play a vital role in supporting the mission of YCGS by providing essential assistance to our staff. YCGS positions are open to current Yale University students, both undergraduate and graduate. We offer a range of student roles to support our diverse program needs. A list of available positions is provided below, and current openings can be found on Yale Student Employment. 

If you’d like to hear about other on-campus geospatial jobs, we encourage you to join our LinkedIn Group, where we share both on- and off-campus geospatial opportunities.

What We Value in Students and Researchers

In our group, we look for people who are curious, rigorous, and adaptable. We value:

• Curiosity and Initiative – asking bold questions, exploring new ideas, and connecting across disciplines.
• Scientific Rigor – grounding insights in data, methods, and evidence.
• Technical Competence – strong skills in statistics, coding, geospatial analysis, and the ability to learn new tools quickly.
• Efficiency and Pragmatism – getting things done within real-world constraints while knowing when to refine and polish.
• Interdisciplinary Thinking – bridging natural sciences, social sciences, the humanities, and applied systems to generate useful insights.
• Communication – making complex results accessible through clear writing, data visualization, mapping, and storytelling.
• Resilience – staying forward-looking and problem-solving even when projects get messy.

We work on projects that connect data science with pressing challenges at the intersection of environment and society. If you’re motivated to learn, collaborate, and create impact, you’ll thrive here.