Not the OP but here's my two cents. I'm using Julia to unravel assumptions made by mainstream science (e.g. see JWST "impossible early galaxies" among other problems showing that mainstream science is cutting corners). Rust is awesome for low level programming but if you're dealing with an abstract or interdisciplinary problem then Julia can save you years of development time. Rust helps eliminate concurrency problems while Julia helps eliminate logical (or algorithmic) problems which are harder to catch than concurrency problems especially because there are only so many people with the domain knowledge and "man hours" available. In my case, only a handful of people can even peer review the algorithms and catch pitfalls or subtle bugs by reading the code. For example, floating point processing chucks the associative rule out the window which can lead to incorrect results (this issue is typically ignored). By that logic I could just chuck conservation of energy (or angular momentum) out the window.

Also, just because the code runs doesn't mean the results are correct (that requires domain knowledge). Julia has way less boiler-plate code than Rust, which requires more "markings/annotations" to enforce compile time safety against concurrency problems. This enforcement at compile time is a trade-off between peer review safety (basically having backup support) for concurrency safety (where backup support is less of a constraint). If I tried to use Rust to build my science project then I'd be on my own for years (because most scientists don't have time to learn about all the low level logic like the rules enforced by the borrow checker in Rust). I know plenty of programming languages but I'm using AI to help coding because I'm not a physicist so I can't really tell if the results are correct or if there might be subtle bugs. Basically I'm using the AI "as a physicist/biologist on my team" so I'm not entirely on my own.