EGU2020: Big Data, Machine Learning and Artificial Intelligence in Glaciology

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EGU2020: Big Data, Machine Learning and Artificial Intelligence in Glaciology

Lea, James

Dear Cryolisters,

 

We would like to invite contributions to a brand new session on ‘Big Data, Machine Learning and Artificial Intelligence in Glaciology’ for EGU 2020 (Vienna, 3-8 May 2020).

 

This session aims to bring together glaciologists who use big data, machine learning and/or artificial intelligence to help share knowledge of different approaches that are currently being taken by the community, and where possible demonstrate their potential transferability in this emergent field.

 

Contributions are invited from those involved in developing and/or applying methods that seek to address these data generation, analytical and visualisation challenges with the aim of gaining greater understanding of past, present and future glacier and ice sheet change.

 

Further information on the session can be found here: https://meetingorganizer.copernicus.org/EGU2020/session/34972

 

The abstract submission deadline is 15th January, 2020, 13:00 CET, though those seeking financial support for attendance should submit their abstract by 1st December.

 

If you have any questions about the session, don’t hesitate to contact myself or any member of session convenors. A few of us will also be at AGU, so if you’re interested in the session do come and say hello!

 

All the best,

 

James Lea, Celia Baumhoer, Leigh Stearns, Soroush Rezvanbehbahani, and Stephen Brough

Session convenors of CR2.4: Big Data, Machine Learning and Artificial Intelligence in Glaciology

 

________________________________
Dr James M Lea
Lecturer in Glaciology

UKRI Future Leaders Fellow

Department of Geography and Planning
School of Environmental Sciences
Roxby Building, University of Liverpool
Liverpool
L69 7ZT

Tel: +44 (0)151 7942856
Email:
[hidden email]
Website: 
https://www.liverpool.ac.uk/environmental-sciences/staff/james-lea/
Twitter:
@JamesMLea

Pronouns: He/Him

Working hours: 9am-5pm, Monday-Friday

 


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