The field of particle physics is approaching a critical horizon defined by challenges including unprecedented data volumes and detector complexity. Upcoming ...

Real-world data (RWD) is transforming clinical research, augmenting existing randomized controlled trial (RCT) data to de-risk studies and improve generalizability. With regulators setting clearer ...

Design engineers can use AI-driven simulation to overcome bottlenecks and accelerate materials discovery with hybrid workflow approaches.

A new low-power sensor node framework combines sensing and machine learning, with the potential to enhance real-time environmental monitoring while optimizing energy efficiency.

Autonomous robots are moving beyond scripted motions and narrow task automation into a new era of adaptable, environment-aware intelligence. At ...

NTN announced that it has integrated machine learning technology into its automated calculation system used for designing 3rd‑generation hub bearings, marking the first use of this approach in the ...

Microelectromechanical systems (MEMS) electrothermal actuators are widely used in applications ranging from micro-optics and ...

The performance of rechargeable batteries is governed by processes deep within their components. A fundamental understanding of electrochemistry, structure–property–performance relationships and the ...

Raja Shankar, VP of machine learning at IQVIA, discusses which AI capabilities sponsors are most likely to adopt first to ...

In this article, we speak to a number of leading mainstream CAD developers to bring you a sneak peek into this future..

2026 will be a transformative year in this area — one where force fields redefine the boundaries of atomistic simulation, making previously unthinkable modeling and discoveries routine. With workflows ...

The Department of Energy (DOE) has released specifications for 26 artificial intelligence (AI) challenges under its Genesis Mission that could reshape how ...