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Energy EfficiencyAtmosphereClear All
PodcastNovember 9, 2023

TILclimate presents: What the heck is El Niño, anyway? (from Outside/In)

TILclimate Podcast
Outside/In logo
PostOctober 27, 2023

Probing the atmosphere, protecting the biosphere

MIT Center for Sustainability Science and Strategy
AGAGE scientists, collaborators and invited guests from research institutions around the world—many representing dozens more researchers at their home institutions—at the ALE/GAGE/AGAGE network’s 45th anniversary conference on October 8-13 at the MIT Endicott House
PostOctober 25, 2023

Smart irrigation technology covers “more crop per drop”

MIT News
Global Engineering and Research (GEAR) Lab students (from left to right) Georgia Van de Zande, Carolyn Sheline, and Fiona Grant pilot a low-cost precision irrigation controller that optimizes system energy and water use at a full-scale test farm in the Jordan Valley.
PodcastOctober 19, 2023

E3: Don't throw away your refrigerator

TILclimate Podcast
PostOctober 18, 2023

Improving Predictability of Wind Power Generation Using Empirical Data

MIT Center for Energy and Environmental Policy Research
Educator GuideOctober 18, 2023

Refrigerants and Climate Change Educator Guide

TILclimate Podcast
a pile of discarded air conditioners, with the TILclimate Guide for Educators logo in front
PodcastOctober 12, 2023

E2: How tackling methane cools the planet fast

TILclimate Podcast
Educator GuideOctober 10, 2023

Methane and Climate Change Educator Guide

TILclimate Podcast
A wetland with a grey sky, with the TILclimate Guides for Educators logo in front
PostOctober 6, 2023

AGU Fall Meeting to encourage the pursuit of Open Science

MIT Center for Sustainability Science and Strategy
Photo: AGU Fall Meeting Central Exhibit Hall (Source: AGU)
PostOctober 5, 2023

New tools are available to help reduce the energy that AI models devour

MIT Lincoln Laboratory
At the Lincoln Laboratory Supercomputing Center, researchers are making changes to cut down on energy use. One of their techniques can reduce the energy of training AI models by 80 percent.

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