Excited to share new paper on Nature Reviews Nature Reviews Earth & Environment 🌈. Want to discover relationships from data? Or answer questions in #Geosci? Or obtain ML-level performance with interpretable processes? Differentiable Modelling could be your answer! rdcu.be/dgzYj (1/n)

It has been one month but I'm very glad to share that I join Stanford HAI and Stanford Earth System Science as an HAI postdoctoral fellow! I will work on synergizing AI, physical models and large data for global water modeling. Look forward to the exciting journey ahead!

🚀I am recruiting 1-2 PhD students to work on using machine learning to integrate hydrologic modeling and field measurements at the Department of Earth Sciences, University of Connecticut UConn Earth Sciences ! Please RT and share! Learn more details at lijingwang.github.io/opportunities/

Working on #XAI / interpretable ML in hydrology? Join our session at #EGU24, co-convened by Dapeng Feng, Marvin, Basil Kraft & Li Lu. Bridging data-driven methods with domain expertise to gain insights into water systems. 📅 Submit by 10 Jan '24 🔗 meetingorganizer.copernicus.org/EGU24/session/…

Yuan Wang: A scientist driven to solve intertwined #climate and #pollution problems Pollution from vehicles, industry, and wildfires affects our future climate and extreme weather events like hurricanes. Read @StanfordDoerr: sustainability.stanford.edu/news/scientist…

Are you interested in 1) the future of water quantity + quality under climate change + extremes; 2) reactive transport models + machine / deep learning tools? Come join our diverse group of learners. "Somewhere, something incredible is waiting to be known." - Carl Sagan

Great opportunities to work with the fantastic hydrologist and mentor!

Super excited to attend #AGU23 in SF. I will give two talks about machine learning to learn hydrologic functions (H22D-07) and hybrid differentiable models for global-scale hydrologic modeling (H33B-01). Looking forward to meeting with old and new friends!

Great opportunities to work on ecohydrology project with the awesome researcher!

How does land-river connectivity shape river water quality in persistent, gradual climate changes & episodic, intense #extremes? New paper NatureClimate reviews overlooked impacts of #climatechange on water quality. nature.com/articles/s4155… SharedIt: rdcu.be/dAAhw

Great postdoc opportunity in the Remote Sensing Ecohydrology Group at Stanford! Alex is an awesome researcher and a thoughtful mentor to work with.

It's great that our paper on differentiable hydrologic modeling to unify ML and physical model is among #TopDownloadedArticle in WRR! Our code release also has more than 1.3K downloads! Thanks for your interest in our work! Chaopeng Shen Kathryn Lawson Jiangtao Liu

Very nice talk from Stephan Hoyer ! It’s great to see differentiable modeling being applied to atmosphere and climate modeling!

Check out our recent article about estimating timescales for ecosystem-level stomatal optimality!

Thanks a lot Nikunj! It’s also great to meet you in person at HydroML!

New Paper Alert! 🚨 Our latest modeling study finds canopy temperature is likely an important omission in interpreting microwave vegetation optical depth. 🌿📡 #VOD #Ecology #MicrowaveRemoteSensing [Link to the paper rb.gy/j6lx2q]

Great opportunities to work with Minghao on climate change and air quality!

Great opportunity to work with Mengze!