Lots of things, but mostly things like designing software architectures for massively parallel codes implementing fancy physics or coming up with ML models to p

Lots of things, but mostly things like designing software architectures for massively parallel codes implementing fancy physics or coming up with ML models to predict complicated properties (things like wear, radiation, or corrosion resistance for which we just do not have any comprehensive model) or explore humongous problem spaces (things like predicting simple properties of very complex materials with 5 to 8 elements and complex microstructures like superalloys). Neither the Physics nor the CS are cutting edge (that’s why we do not necessarily reject people with limited experience in Physics, though they need to show motivation and abilities to learn); what is is the combination of both. We’d like to be as close to the cutting edge on ML as possible, though, because it’s a significant competitive advantage to be able to use fancy new techniques before our friendly competitors. But as I said it seems to be easier to train a Physics or Chemistry undergrad to get some feeling about how ML works than to train a CS undergrad to have some intuition about the Physics. And intuition is critical to detect when models hallucinate and get off the rails.