All of these have some exceptions, but are for the most part true:

high energy physics

lots of computational bio

fluid dynamics

rendering

In general, python and matlab really struggle in problems where you want maximum performance, but the most efficient algorithms aren't vectorized. In some cases, this is solvable by writing python libraries in C/C++, but especially in scientific fields, the end users are often the same people writing the algorithms, so they don't gain much from a python library if they have to do all the hard work in C++ anyway. Julia gives them a way more productive dev experience while still having a good user experience.