Sustainable technology isn’t a theoretical future field. It’s a set of live projects happening right now, in labs, on farms, on factory floors, and in the ocean, run by teams that blend environmental knowledge with computing skill.
Here are four current examples of the kind of work students in this space could be part of.
1. Stress-Testing the Grid Before It Breaks: NREL’s ARIES Platform
At the National Laboratory of the Rockies campus in Colorado, researchers are running one of the most advanced energy simulation platforms in the world. ARIES (Advanced Research on Integrated Energy Systems) combines real hardware, a 44-petaflop supercomputer, and virtual emulation to model how the power grid behaves as more solar, wind, battery storage, and electric vehicles get added to it.
In 2025, ARIES partnered with a data center company to model grid-aware controls on a virtual 70-megawatt system, testing whether data centers can actually help stabilize the grid instead of straining it. In a separate experiment, engineers connected a real generator to an emulated data center to study, for the first time, how the constant power fluctuations from AI computing physically wear down grid equipment.
This is the intersection point: energy systems, computing infrastructure, and climate goals, all running through the same lab. Students headed into grid modernization, energy analytics, or clean energy policy would be working with exactly this kind of platform.
2. Teaching Machines to Tell Weeds From Crops
John Deere’s See & Spray system uses cameras and computer vision mounted on farm sprayers to identify weeds in real time, at highway speed, across corn, soybean, cotton, and now wheat and barley fields. The system takes an image every few inches, runs it through an object detection model trained to recognize dozens of weed species, and fires a nozzle only where a weed actually is.
The results are concrete: farmers using the technology have cut herbicide use by roughly half, and Deere reports the system saved an estimated 31 million gallons of herbicide across 5 million acres in a single year. The company is now expanding the same camera network into “See & Scout,” turning every spraying pass into a data-collection pass that builds weed maps and crop stand counts automatically.
This is a clear example of environmental problem-solving through applied computer science: less chemical runoff, lower cost, and a real machine learning pipeline running in the field.
3. Turning Dead Batteries Into Grid Power (and Powering AI With Them)
Redwood Materials, based in Nevada, has built the largest lithium-ion battery recycling operation in North America, processing around 90% of all lithium-ion battery material recycled in the country. On the consumer side, the company just rolled out a statewide network of smart battery recycling bins across Nevada, engineered with temperature sensors and fire-suppressant material to make household battery recycling safe.
The more striking project is on the industrial side: Redwood is repurposing used EV batteries, batteries that still hold real capacity but no longer meet a car’s standards, into large-scale energy storage that powers AI data centers on solar power. It’s a direct link between the electric vehicle boom, the AI computing boom, and the circular economy for materials.
For students, this is a look at what a career combining materials science, data systems, and clean energy actually looks like day to day.
4. Listening to the Ocean With AI
NOAA and a network of regional ocean observing programs are using machine learning to make sense of an enormous, messy stream of ocean data. One project, led with the University of South Florida, is building an AI classifier nicknamed GUARDIAN that can process acoustic recordings from moored buoys, ships, and underwater vehicles to automatically detect the calls of endangered whale species. A related effort is training models to strip out boat noise from thousands of hours of underwater recordings so researchers can actually hear the marine life underneath it.
Separately, NOAA’s AI Center has built “Samudra,” a model trained to simulate global ocean behavior, predicting sea surface height, currents, temperature, and salinity from the surface to the deep ocean.
For a coastal state like Maine, this is about as close to home as sustainable computing gets: real buoys, real whales, real climate data, and a growing need for people who can code as well as they understand marine ecosystems.
Build the Technology and Computing Skills This Field Is Already Hiring For
The four projects above aren’t outliers. They’re evidence of a hiring pattern that’s still forming, which is exactly why timing matters here. Labs, farm equipment makers, battery recyclers, and ocean researchers are all discovering the same gap at once: people who understand environmental systems and people who can build computing solutions are usually two different hires, and organizations are realizing they need one person who can do both.
That gap won’t stay open forever. As AI and computer vision get folded into energy grids, agriculture, materials recovery, and climate monitoring, the early graduates who can speak both languages will define what these roles look like for everyone who follows. Getting into the field now means training alongside the technology as it’s built, not catching up to it after the job descriptions have already solidified.
Unity’s online MS in Sustainable Technology and Computing program is designed around that overlap. The program builds environmental science fundamentals alongside real computing skill, so graduates walk into interviews already fluent in both sides of the work these projects represent. It’s delivered fully online, giving working professionals a path to move into this space without pausing their current career.
