News and events

What's currently happening in the network

More about ELLIS activities available on the ELLIS news and ELLIS events page.
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New & Events

News
Inside the ELLIS PhD Program: PhD student Marvin Schmitt shares impressions from his research visit at Aalto University in Helsinki
published on
August 28, 2024

Exchanging ideas face-to-face, getting to know new research environments and initiating collaborations are crucial for scientific excellence and innovative research in the cutting-edge field of modern AI.

To foster exchange among scientists in Europe, several projects building on ELLIS like the EU-funded projects ELISE, ELSA and ELIAS, and the graduate school ELIZA support junior researchers like ELLIS PhD students and senior researchers with mobility grants.

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News
“Machine learning methods have the potential to drastically reduce the time and cost involved in molecular discovery.”
published on
August 28, 2024

The ELLIS Program ‘Machine Learning for Molecule Discovery’ works at the intersection of machine learning and molecular science in Europe. It focuses on establishing a dialogue between domain experts and machine learning researchers to ensure that machine learning positively impacts real world scenarios.

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News
“With advances in AI, we have better tools to act against the climate crisis”
published on
July 22, 2024

From the “ELLIS/ELISE AI for Learning Weather and Climate” workshop, Gustau Camps-Valls, Co-Director of the ELLIS Program ‘Machine Learning for Earth and Climate Sciences’, highlights the crucial role of Artificial Intelligence in addressing climate change.

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Events
ELLIS Workshop on Robustness in Large Language Models (RobustLLMs) in Oxford
published on
July 10, 2024

ELLIS Workshop on Robustness in Large Language Models (RobustLLMs) is a two-day event hosted by the Oxford Department of Statistics at Keble College, Oxford and co-organised by ELISE. The workshop will feature keynotes and invited talks, discussions and poster sessions focused on the role of robustness in improving factuality and reasoning, defending against adversarial inputs, enhancing reliability for real-world applications, and dealing with hallucinations in LLMs.

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