Physics
Physics

Physics

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Five with MIT ties elected to National Academy of Medicine for 2025

Professors Facundo Batista and Dina Katabi, along with three additional MIT alumni, are honored for their outstanding professional achievement and commitment to service.

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MIT Schwarzman College of Computing launches postdoctoral program to advance AI across disciplines

The new Tayebati Postdoctoral Fellowship Program will support leading postdocs to bring cutting-edge AI to bear on research in scientific discovery or music.

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Scientists use generative AI to answer complex questions in physics

A new technique that can automatically classify phases of physical systems could help scientists investigate novel materials.

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Starship Soars: A Powerful Turning Point for Humanity’s Reach in Space

Despite these challenges, the successful Starship launch is a monumental achievement. It represents a significant leap forward in space exploration technology and has the potential to revolutionize how we interact with space. Elon Musk, whether admired…

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Gosha Geogdzhayev and Sadhana Lolla named 2024 Gates Cambridge Scholars

The MIT seniors will pursue graduate studies at Cambridge University.

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Technique could efficiently solve partial differential equations for numerous applications

MIT researchers propose “PEDS” method for developing models of complex physical systems in mechanics, optics, thermal transport, fluid dynamics, physical chemistry, climate, and more.

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From physics to generative AI: An AI model for advanced pattern generation

Inspired by physics, a new generative model PFGM++ outperforms diffusion models in image generation.

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Fast-tracking fusion energy’s arrival with AI and accessibility

MIT Plasma Science and Fusion Center will receive DoE support to improve access to fusion data and increase workforce diversity.

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Artificial intelligence for augmentation and productivity

The MIT Schwarzman College of Computing awards seed grants to seven interdisciplinary projects exploring AI-augmented management.

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A simpler method for learning to control a robot

Researchers develop a machine-learning technique that can efficiently learn to control a robot, leading to better performance with fewer data.