SCIENCE AI Centre

Previous Events

24 Feb. - 12 May 2023

Executive Masterclass – Ethical and Responsible AI

This Masterclass is for decision makers and professionals working in the Finance and Fintech industry, who seek insights in working with Artificial Intelligence in a responsible and trustworthy way.

10 Feb. 2023, 13:00-14:00

AI Seminar: Platform intelligence in news

Join us for a talk by Stine Lomborg and Rasmus Helles, Associate Professors in the Department of Communication and part of Center for Tracking & Society, University of Copenhagen will give a talk. Everybody is welcome to attend.

3 Feb. 2023, 11:00-12:00

AI Seminar: Accurate somatic mutation detection using weakly supervised deep learning

Anders Skanderup, Principal Investigator at the Genome Institute of Singapore, will give a talk. Everybody is welcome to attend.

27 Jan. 2023, 13:00-14:00

AI Seminar: Explaining molecular properties with natural language

Join us for a talk by Dr. Andrew White! Everybody is welcome to attend.

25 Nov. 2022, 12:00-13:00

AI Seminar: Transforming the power grid to a zero-carbon future with data, machine learning and automation

Casper Kaae Sønderby, Co-founder of twig.energy, will give a talk titled "Transforming the power grid to a zero-carbon future with data, machine learning and automation".

23 Nov. 2022, 13:30-14:30

Pioneer Centre for AI Talk: Sergio Escalera

11 Nov. 2022, 13:00-14:00

AI Seminar: Mysteries in multi-task learning and input representations for language models

On 11 November Rob van der Goot will give a talk titled "Mysteries in multi-task learning and input representations for language models".

11 Nov. 2022, 9:30-17:00

Learning through XR

Hear cases and exchange best practice on how we can create the best learning experiences in Virtual Reality, Augmented Reality or Mixed Reality – the world of XR.

7 Nov. - 8 Nov. 2022

DDSA Conference: Danish Data Science 2022

4 Nov. 2022, 15:00-16:00

Pioneer Centre for AI Talk: Dr. Enzo Ferrante

Pioneer Centre for AI talk by Enzo Ferrante: Fairness of machine learning in medical image analysis