RSS Statistical Computing Section seeks new committee members

The RSS Statistical Computing Section is looking for three new members to serve on its committee from 2020.

The section is interested in the application of computing in modern statistical analysis and organises 3-4 events a year to promote developments and research in statistical computing. Recent events include 'Machine learning in astronomy' and 'Statistics in cyber-security'.

As a committee member, you will get to actively engage with people who are interested in computational issues and design of software for statistical computing and help achieving of the section's aims: act as a national focus for statisticians involved in theoretical and practical aspects of statistical computing.

Since 2018, the section is host to the Computational Statistics and Machine Learning network, and therefore, for the 2020 committee, we particularly welcome applicants with a background in computational statistics and machine learning. We are looking for volunteers who are keen to contribute new ideas for section events, actively help organise events and willing to represent the section at RSS and external meetings. We usually hold three committee meetings per year, which can be attended in person at the Society's Errol Street offices (travel expenses paid), or remotely by teleconference. Much of our core business is communicated over email and instant messaging platforms.

If you are interested in this role, and would like to discuss it further, please contact the section's chair, Theo Kypraios.

If you would like to put yourself forward for joining the committee, then please send an email (subject line 'statistical computing section committee 2020') to Amaka Nwagbara by Wednesday 13 November 2019, with a paragraph (up to 250 words) summarising your background, why you would like to join, and what you will contribute to the committee. Please note that to take up a position you will need to be a fellow of the RSS.

The RSS is committed to ensuring an inclusive and welcoming environment for all who care about good data and statistics.

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