Research Software Engineers

By Mark Woodbridge, Research Software Engineer group lead at Imperial College London.

This is the second in a series of posts describing activities funded by our RSE Cloud Computing Award. We are exploring the use of selected Microsoft Azure services to accelerate the delivery of RSE projects via a cloud-first approach.

Registration for RSE 2018 in Birmingham in September is now open  

The last two conferences sold out, so the organising committee added 50% more tickets this year. However, tickets go fast, so please sign up early. 

By Jeremy Cohen, Imperial College London (editor). See Contributors section at the end of the report for full list of contributors

By Martin Callaghan, University of Leeds, Daniel S. Katz, University of Illinois, Alexander Struck, Cluster of Excellence Image Knowledge Gestaltung, HU-Berlin, and Matt Williams, University of Bristol, 

By Anna Krystalli, University of Sheffield, and Toby Hodges, EMBL Bio-IT

By Anna Krystalli, University of Sheffield, and Toby Hodges, EMBL Bio-IT

By Anna Krystalli, University of Sheffield, and Toby Hodges, EMBL Bio-IT

Following the success of the Second Conference of Research Software Engineers, the RSE conference 2018 will take place at the Bramall Building at the University of Birmingham, on 3rd-4th September 2018. RSE18 is the perfect opportunity to promote your products and services to research software engineering leaders and decision makers (present and future) at the only conference purpose built for RSEs.

When I first started thinking about how we could create a career path for Research Software Engineers (RSEs) in academia, I assumed we would have to persuade university management to change their policies and make it possible, or at least much easier, for researchers to retain RSEs within their groups. The actual solution has been somewhat different, and much more effective.

By Matt Archer, Paul Brown, Stephen Dowsland, David Mawdsley, Amy Krause, Mark Turner (order is alphabetical).

So… you’ve just started on an exciting new data science project, but you know nothing about the domain you’re working on. Besides briefly panicking, how do you get up to speed on the area you’re working on?

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