Department of Computer Science

Previous Events

28 Apr. 2023, 12:30-13:30

AI Seminar: Machine Learning Powered Inference in Cosmology

Join us for a talk by Pablo Lemos. Everybody is welcome to attend.

26 Apr. 2023, 13:00-16:00

PhD defence by Per Steffen Czolbe

Join us when Per Steffen Czolbe will defend their PhD thesis titled 'Development and Clinical Evaluation of Deep Learning Models for Personalized Breast Cancer Screening'.

17 Apr. 2023, 10:00-11:00

AI Seminar: Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection

Join us for this AI Seminar by Martin Jankowiak, machine learning scientist at Generate Biomedicines. Everybody is welcome to attend.

24 Mar. 2023, 13:00-14:00

AI Seminar: XAI for representation learning

Join us for an online talk by Robert Jenssen, Director of SFI Visual Intelligence and Professor at UiT The Arctic University of Norway. Everybody is welcome to attend.

9 Mar. 2023, 15:00-18:00

PhD defence by Dustin Brandon Wright

Join us when Dustin Brandon Wright will defend their PhD thesis titled 'Machine Understanding of Scientific Language'.

1 Feb. 2023, 14:00-17:00

PhD defence by Yuhu Liang

On 1 February Yuhu Liang will defend his PhD thesis titled 'In pursuit of gene variation of consequence to human health and disease'.

1 Feb. 2023, 14:00-17:00

PhD defence by Yuhu Liang

On 1 February Yuhu Liang will defend his PhD thesis titled 'In pursuit of gene variation of consequence to human health and disease'.

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.

26 Jan. 2023, 13:00-17:00

PhD defence by Chloé Alice Florence Rouyer

On 26 January Chloé Alice Florence Rouyer will defend her PhD thesis titled 'Theoretical Foundations of Learning with Worst-Case and Easy Data'.

26 Jan. 2023, 13:00-17:00

PhD defence by Chloé Alice Florence Rouyer

On 26 January Chloé Alice Florence Rouyer will defend her PhD thesis titled 'Theoretical Foundations of Learning with Worst-Case and Easy Data'.