As you said yourself, you have no performances problem with R and Python, so I do not think you need to learn Julia.

However, in fields dealing with larger datasets (e.g. genetics, astrophysics, ...), Python and R are real bottlenecks in data processing pipelines, and are thus often replaced by programs in C/C++/Java/etc. Having a language with the expressiveness and dynamism of Julia on the one hand, and performances in the same order of magnitude than C++ in the other hand is a huge plus.

Do they use Fortran in those fields?

In astrophysics yes (although I only follow the fields through friends), quite a lot of linear algebra/physics reference libraries are in Fortran, and new programs are still being developed in the more modern versions of it.

In Bioinformatics, not at all. Or at least in no direct way that I'm aware of. Sure, R packages indirectly call upon Fortran libraries (LAPACK et al.), but user facing programs are mostly, from my experience, written in C, C++, Java, Python & Perl.

Great, the answer is then: You should give it a try, to a certain extend "Why Julia is nice" is easier experienced than explained.