I joined BAS as a member of the 2016 wintering team at Halley as Data Manager, having worked extensively in industry in various roles covering “DevOps”, software development and IT infrastructure engineering.  I spent the austral winter of 2017 back in Cambridge assisting with technological development for the wintering of the station without human presence. I spent my second summer at Halley in a dual role as Data Manager and Science Coordinator (2017-2018), followed by a winter in Rothera and another summer in Halley, working with the summer team to automate the base for the austral winter in 2019.

In March 2019 I joined the BAS IT Group in Cambridge as a Unix Engineer, working groups across the organisation to support and improve our systems and storage administration, project management, identity management, remote science and scientific computing.

In June 2021 I moved into the role of Research Software Engineer to work on improving software engineering both within the organisation and across the environmental sciences. This has seen me involved in many more projects as well as collaborations with other NERC centres and our partners at The Alan Turing Institute, among others. Additionally, I’m a 2022 Software Sustainability Fellow and certified Carpentries Instructor. You can also find me on GitHub.

Below are some rambling answers to some questions I thought worth answering to myself.

Why do I choose to work for BAS? 

The aim of my being at BAS is to do something that makes me feel like I’m helping science to progress. The best way I can envisage doing this, is by supporting those who investigate our natural environment, because that’s where there’s a disproportionately high impact from human activities in relation to investment in conservation. We don’t take care of our environment as a species, and that will inevitably come home to roost, for those who least deserve to deal with it first and foremost. 

Why am I doing what I do at BAS? 

I’m a Research Software Engineer. I thought this is a bit hand wavey, but it is a valuable moniker given I used to be a software engineer in industry. Prefixing “research” helps in doing what I want to do where I do it now! I want to highlight inadequacies and propose solutions, in the research community’ employment of technology, to the scientific community and those who support it. To start this, I keep doing (trying to anyway) software engineering, helping people who are trained to investigate questions about the environment get better at using software. Better software really does equal better research, because it’ll be adaptable, understandable and reusable. It’s only fair that scientists, who often are taught how to hack software but without knowing how software engineering works broadly, get that gap filled in. 

Next, I look at how I am doing (research) software engineering. Is it effective (often no) and am I working to my maximum potential (also often no?) If the answers to these questions are nearer no than yes (brackets offer a nice sub-narrative, they are) then how can I improve things, so that my life is more useful to others and they end up asking me less questions. Some of the initiatives I’m working on towards this are easier environments, promoting reuse and being better at communicating software engineering best practices.  

Finally, I look at how we’re supported as software engineers by the research community. Is that effective (mmmmmm, sometimes) and is it moving in the right direction (mostly yes) fast enough (almost certainly not)? If the answers here aren’t a resounding yes (they’re not) then I should reflect upon the fact that my laziness and impact as an individual who’s been working in technology for 20+ years is part of the cause of that problem and I should make amends. I’m lucky that I can see potential for improvement, so I should stick my neck out and try and disrupt, then improve, the system. To this end I seek to promote interoperability, community and sustainability (through efficiency and parsimony) by annoying people who are paid more than me until they’ll let me change things for the better. 

• Bowler, E., Byrne, J., Leclerc, L., Roberto-Charron, A., Rogers, M., Cavanagh, R., Harasimo, J., Lancaster, M., Chan, R., Strickson, O., Wilkinson, J., Downie, R., Hosking, J., & Andersson, T. (2025). Pan-Arctic 93-day sea ice concentration forecasts from the IceNet model and mappings between sea ice concentration and Dolphin and Union caribou sea ice crossing-start times (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/8738b3cb-52c7-4b36-aa6d-6e15c0b46ba4

• Williams, C., Arthern, R., & Byrne, J. (2024). Forward simulations of the Amundsen Sea Sector of West Antarctica at different resolutions produced from the ice sheet model WAVI (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/4de39bc0-fc2b-4232-ac39-3cc1fd723f64

• Andersson, T., & Hosking, J. (2021). Forecasts, neural networks, and results from the paper: ‘Seasonal Arctic sea ice forecasting with probabilistic deep learning’ (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/71820e7d-c628-4e32-969f-464b7efb187c

1. Improving how we measure Antarctic sea ice

   QUASAR uses AI to improve Antarctic sea ice measurements from satellites, making climate data more reliable for scientists tracking changes in polar regions.

   Read more of: Improving how we measure Antarctic sea ice

   

2. Understanding Greenland’s changing ice

   GIANT is a pioneering science project that will test the potential for early warning of a critical climate tipping point.

   Read more of: Understanding Greenland’s changing ice

   

3. AI for smart conservation

   The AI for smart conservation project combines sea ice forecasts, satellite data, and GPS tracking to create early-warning systems for Arctic wildlife.

   Read more of: AI for smart conservation

   

4. Digital Twins of the Polar Regions

   This project is developing digital twins of Antarctic and Arctic environments and resources. A digital twin makes it possible to test “what if” questions far more quickly than traditional computer models.

   Read more of: Digital Twins of the Polar Regions

   

6. Machine Learning for Sea Ice Forecasting

   IceNet is a deep learning system that forecasts Arctic sea ice. The system is trained on climate simulations and observational data. It outperforms state-of-the-art dynamical models, especially for extreme sea ice events.

   Read more of: Machine Learning for Sea Ice Forecasting

   

7. Logist AI for environmentally aware decision support

   AMOP is developing Artificial Intelligence methods that aim to optimise the efficiency of Antarctic field operations, while maximising science delivery

   Read more of: Logist AI for environmentally aware decision support

   

8. Halley Automation

   This innovative, multi-year, project created a suite of autonomous scientific instrumentation around Halley Research Station, enabling data collection even when the station may be unoccupied.

   Read more of: Halley Automation

   

9. Brunt Ice Shelf movement

   British Antarctic Survey is monitoring glaciological changes on the Brunt Ice Shelf, home to Halley Research Station.

   Read more of: Brunt Ice Shelf movement