Research Scientist in ML for Climate Modeling at AI2

The climate modeling team at the Allen Institute for Artificial Intelligence is looking to hire a full-time Research Scientist. This is an exciting opportunity for someone with machine learning and numerical modeling experience to contribute to our ambitious, cutting-edge research program through novel applications of ML to improve weather and climate models. There are still a few weeks left to apply!

See here for the full job description and application.

1 Like

Thanks for posting this! A question I wanted to ask after reviewing the JD:

  • You must have a Ph. D. in computer or computational science, atmospheric science or a related geophysical science, or applied mathematics/statistics, with at least one first-authored publication making extensive use of ML. Formal graduate-level ML coursework is a plus.

I think this is a really interesting requirement. Given the relatively slow uptake of ML/AI in the geosciences until the past few years, are the AI2 folks at all concerned that this could disqualify a large number of candidates except for recent graduates? For example, I’ll use myself as an example (although I understand full well I am likely not an ideal candidate for the role!): my doctorate is only 5 years old, and virtually all my practical experience with AI/ML has been in industry, where I unfortunately didn’t have the opportunity to publish papers about any of my work. Nor was there much opportunity – or support for what limited opportunities did exist – to pursue graduate-level coursework in ML (I guess those handful of grad-level stats courses could be spun?).

Anyways, curious if you all are seeing a good candidate pool – especially given the strong desire for computational and atmospheric modeling experience? There are a lot of workforce concerns in the geosciences right now regarding the extremely high demand for cross-disciplinary AI/ML experience but limited supply… is that your all’s experience?

Thanks Daniel – those are good questions. For clearly excellent candidates who do not meet all the requirements, exceptions can be made, particularly if they can demonstrate some form of equivalent experience (e.g. AI/ML work in industry instead of a first-author publication). We acknowledge some of these requirements may be harder to come by in the geoscience community, though we are also interested in candidates from other backgrounds (e.g. computer or computational science, or applied math/statistics) where those requirements may be more common.