Safe way for parallel random sampling in python3

I need to repeat N times a scientific simulation based on a random sampling, easily:

results = [mysimulation() for i in range(N)]

Since every simulation require minutes, I'd like to parallelize them in order to reduce the execution time. Some weeks ago I successfully analyzed some simpler cases, for which I wrote my code in C using OpenMP and functions like rand_r() for avoiding seed overlapping. How could I obtain a similar effect in python?

I tried reading more about python3 multithreading/parallelization, but I found no results concerning the random generation. Conversely, numpy.random does not suggest anything in this direction (as far as I found).

Thanks in advance