News

EPFL researchers have developed a computational method to explicitly consider the impact of water while designing membrane receptors with enhanced stability and signaling, paving the way for novel drug discovery and protein engineering.

By applying techniques from explainable artificial intelligence, engineers can improve users’ confidence in forecasts generated by artificial intelligence models. This approach was recently tested on wind power generation by a team that includes experts from EPFL.

EPFL scientists have used deep learning to design new proteins that bind to complexes involving other small molecules like hormones or drugs, opening up a world of possibilities in the computational design of molecular interactions for biomedicine.

The Swiss National Science Foundation (SNSF) has awarded three “SNSF Advanced Grants” to EPFL researchers, including Florent Krzakala in the School of Engineering.

The use of generative artificial intelligence in protein design stands to revolutionize new drug development. EPFL ambitions putting together a consortium to further explore this avenue.

A researcher at EPFL working at the crossroads of neuroscience and computational science has developed an algorithm that can predict how surprise and novelty affect behavior.

EPFL researchers have developed 4M, a next-generation, open-sourced framework for training versatile and scalable multimodal foundation models that go beyond language.

New research from EPFL demonstrates that even the most recent Large Language Models (LLMs), despite undergoing safety training, remain vulnerable to simple input manipulations that can cause them to behave in unintended or harmful ways.

Artificial intelligence (AI) may be a weapon of mass disinformation, but a recent report has demonstrated that its impact thus far has been limited.

EPFL’s Anastasiia Koloskova has won this year’s ELLIS PhD award, which recognizes European dissertations in the area of artificial intelligence and machine learning-related fields.