Climate Change AI

The CCAI Summer School starts next week! The program will feature 24 content modules about AI applications for climate action. Today, we’re spotlighting our upcoming lectures & tutorials on AI for Built Environment & Energy.⚡️ Learn more & register here: climatechange.ai/events/summer_…

❓With the CCAI Summer School starting soon, we're answering some frequent questions. Overall, we aim to make the program broadly accessible: All lectures will be recorded, & participants will have access even after the program. Find the full FAQ here: climatechange.ai/events/summer_…

Register for this year’s CCAI Summer School, which features 24 content modules about AI applications for climate action. Today, we’re spotlighting our upcoming lectures and tutorials about AI for Land, Food, & Nature.🌱🚜 Learn more and register here: climatechange.ai/events/summer_…

Register for this year’s CCAI Summer School, which features 24 content modules about AI applications for climate action. Today, we’re spotlighting our upcoming lectures and tutorials on AI for Society, Policy, & Economics. 📊 Learn more and register here: climatechange.ai/events/summer_…

The program features 24 content modules, and today, we’re spotlighting the expert researchers and practitioners who are developing educational content about AI for Earth & Climate Systems. Learn more & register: climatechange.ai/events/summer_…

Register for this year’s Climate Change AI Summer School, which will feature a variety of lectures and hands-on tutorials about AI applications for climate action. 🌍🌱

The CCAI Virtual Summer School 2026 begins this month, starting on July 19! Join to learn about different applications of #AI for #climate action from expert researchers and practitioners. You can learn more about the program and register here: climatechange.ai/events/summer_…

We're so excited to see our community grow larger day by day!🌱💚 Thanks to 30,000 LinkedIn followers & 15,000 Community Platform members.🎉 Haven't joined yet? Explore the different ways in which you can stay connected to Climate Change AI here 👉 climatechange.ai

UW ECE Assistant Professor June Lukuyu is part of a multi-organization team using machine learning to make power systems planning faster, more flexible & more accessible across the Global South. Selected as 1 of 12 projects worldwide by @ClimateChangeAI ece.uw.edu/spotlight/ai-f…

Learn more and register for our summer school here 👉climatechange.ai/events/summer_…

We’re excited to announce the lecturers and tutorial creators for our Virtual Summer School! The program features 24 content modules, and today, we’re spotlighting the export researchers and practitioners who are developing educational content about AI & Climate foundations.🚀🎉

Topics that will be covered range from AI & Climate Foundations to applications of AI for Climate Science, Biodiversity, Public Health, Agriculture, Energy and many more. You can learn more about the program and register here 👇 climatechange.ai/events/summer_…

Register for the Climate Change AI Virtual Summer School 2026 starting July 19! The program will feature an exciting lineup of lectures and tutorials exploring the intersections of artificial intelligence and climate action. 🎉🤖🌱

We thank our partners @AfriClimateAI , @climateinteract and @DataDotOrg for their support.

Excited to announce the fourth @ClimateChangeAI Summer School (virtual, July 19–August 22, 2026)! 🎉 Are you an #AI or #DataScience expert who wants to tackle #ClimateChange? Are you a #climate expert trying to use #ML in your work? Register here 👉 climatechange.ai/events/summer_…

Our team at @MSFTResearch is looking for a 2y ML post-doc to re-think materials design using AI. This is an exceptional opportunity to join our ambitious mission of solving some of the most pressing challenges that exist today. #ai4science #materials ⬇️See link below ⬇️

We are VERY excited to announce the call for applications has just been released for the 5th CV4Ecology Short Course!! This intensive program trains ecologists and conservation practitioners to develop their own AI tools for their own data. Tell your friends! (link below)

🚨 Hiring a Postdoctoral Researcher in Scientific Machine Learning (SciML) at Johns Hopkins University 🚨 Was your PhD about cutting-edge Scientific Machine Learning (SciML)? If yes, I’d be glad to hear from you. Role: Full-time on-site role for a postdoc at the Department of Civil and Systems Engineering and the Ralph O'Connor Sustainable Energy Institute (ROSEI) at JHU. The role focuses on foundational research in SciML for constrained optimization and control with applications in large-scale energy systems. Qualifications: PhD degree in Control, Computer Science, Applied Math, Operations Research, Industrial Engineering, or a related field Rigorous applied math foundations Prior research experience in learning to optimize (L2O) and decision-focused learning Prior research experience in physics-informed machine learning (PIML) and neural operators Proficiency in at least one programming language (Python, Julia) Proficiency in the use of differentiable programming libraries (PyTorch, Jax) Demonstrated software development skills via open-source contributions If you're passionate about advancing research in SciML for decision-making with a real-world impact, I invite you to apply. 🔬 For more details about my research, visit: solaris-jhu.github.io/solaris-website github.com/SOLARIS-JHU engineering.jhu.edu/case/faculty/j… drgona.github.io 📧 Interested candidates can send their CVs to jdrgona1@jh.edu.

We're excited to announce our latest rounds of Innovation Grantees! Check out funded projects here: climatechange.ai/press_releases… 🌱🌍 Made possible thanks to support by the Quadrature Climate Foundation, @GoogleDeepMind, @Gmethanehub & @FutureEarth.

New paper  in Energy and Buildings: “Physics vs. Structure: A Systematic Benchmark of Learning Strategies for Multi-Zone Building Thermal Dynamics.” We provide an extensive benchmark that systematically compares a range of modeling approaches, from linear models to neural ODEs and physics-constrained models, for data-driven modeling of thermal dynamics in buildings. Besides prediction accuracy, we thoroughly quantify computational cost and physical consistency of the models, which is critical for HVAC control applications. paper: sciencedirect.com/science/articl… All the experiments have been made possible through the Neuromancer scientific machine learning library: github.com/pnnl/neuromanc… Thanks to my PNNL colleagues Soumya Vasisht, Cary Faulkner, Elad Michael, Aaron Tuor, and Draguna Vrabie for this fruitful collaboration, as well as the funding from @ENERGY that supported this work.