3D-printing platform rapidly produces complex electric machines
Overcoming challenges of 3D printing with multiple functional materials, MIT researchers fabricated an electric linear motor in hours.
Overcoming challenges of 3D printing with multiple functional materials, MIT researchers fabricated an electric linear motor in hours.
Seven faculty members, along with 12 additional alumni, are honored for significant contributions to engineering research, practice, and education.
Through research with MIT D-Lab, MIT engineering student Kiyoko “Kik” Hayano worked with Keo Fish Farms to build a model for regenerative water systems.
MIT researchers used a large language model to optimize the genetic sequences of proteins manufactured by yeast, making production more efficient.
Hertha Metals, founded by Laureen Meroueh SM ’18, PhD ’20, uses an electric arc furnace, powered by natural gas and electricity, to melt and reduce low-grade iron ore in a single step.
Design leader brings extensive interdisciplinary track record to key role supporting faculty across the Institute.
Associate Professor Rafael Gómez-Bombarelli has spent his career applying AI to improve scientific discovery. Now he believes we are at an inflection point.
Driven by overuse and misuse of antibiotics, drug-resistant infections are on the rise, while development of new antibacterial tools has slowed.
MagMix, an onboard mixing device, enables scalable manufacturing of 3D-printed tissues.
MIT Sports Lab researchers are applying AI technologies to help figure skaters improve. They also have thoughts on whether five-rotation jumps are humanly possible.
The flexible material could enable on-demand heat dissipation for electronics, fabrics, and buildings.
Removing just a tiny fraction of the crowdsourced data that informs online ranking platforms can significantly change the results.
Former Chemical Engineering Practice School director recognized by the National Academy of Engineering for decades of leadership advancing immersive, industry-centered learning at MIT.
EnCompass executes AI agent programs by backtracking and making multiple attempts, finding the best set of outputs generated by an LLM. It could help coders work with AI agents more efficiently.
Based on a virus-like particle built with a DNA scaffold, the approach could generate broadly neutralizing antibody responses against HIV or influenza.