- UK: Met Office https://www.metoffice.gov.uk/about-us/careers/vacancies)
- France: Institut Pierre Simon Laplace https://www.ipsl.fr/en/ (their job page is busted)
- Germany: Max Planck Institute https://www.mpimet.mpg.de/en/institute/opportunities/
- Netherlands: KNMI https://www.werkenvoornederland.nl/organisaties/ministerie-v...
I'm sure there are more, but these are the ones that I've worked with in the past. European climate research agencies seemed to have their act together a lot better than US ones; before I left the industry, they were much further on the path of moving computation to the commercial cloud (instead of trying to keep up in the supercomputer arms race, which always seemed like a losing proposition to me), and I think ML has been increasingly integrated into climate models as a way to approximate complex dynamical systems.
In terms of which of these labs had their head screwed on straight from a technical side, it was probably KNMI / Met Office, followed by Max Planck and then IPSL.
http://worrydream.com/ClimateChange/
One of the clear recommendations is - contribute to Julia. A lot of what Bret said in 2015 has actually panned out. Julia has become a powerful language for scientific computing and machine learning. As a result it is being used in climate projects such as Climate Machine (MIT and Caltech). The Julia Lab at MIT participates in this project:
Climate Machine: https://clima.caltech.edu/ Github: https://github.com/climate-machine/ Julia Lab: http://julia.mit.edu/
Contributions to Julia packages (compiler, stdlibs, math packages, ML packages, parallel computing) will end up finding their way into climate research, because of the extensive reuse of code within the Julia ecosystem. Specifically capabilities such as Zygote.jl (https://github.com/FluxML/Zygote.jl) for differentiable programming have the potential to dramatically make it easy to apply ML techniques to scientific codebases. Compiler contributors are hard to come along, so all contributions to compiler technology are incredibly valuable. The DiffEqFlux.jl ecosystem in Julia is a good example of combining mechanistic models with ML (https://github.com/JuliaDiffEq/DiffEqFlux.jl). Hop on to the Julia slack channel or discourse to dig in deeper.
Another thing Bret Victor speaks about in his blog post is working with agencies such as ARPA-E on advanced projects. Julia Computing is participating in an ARPA-E project to bring these capabilities to many energy related simulation and development technologies. This press release gives a broad idea:
ARPA-E press release: https://www.energy.gov/articles/department-energy-announces-... Funded projects: https://arpa-e.energy.gov/sites/default/files/documents/file... Julia Computing press release: https://juliacomputing.com/communication/2019/12/09/arpa-e.h...