Actually, Perl was a quite natural choice for a while for some computational biology / bioinformatics workloads. In particular, defining scripts where you expect a sequence (like DNA) as input and a filtered or modified sequence as output allowed for processing pipelines that just flowed nicely: script1 | script2 | final_script.
It's been a while since I was in that field, but I suspect those kind of low level operations are now heavily optimized in faster languages as sequences to operate on became longer and operations more complex.
I chose to focus on computational biology because I had an amazing professor that I wanted to work with. That being said, an amazing professor != amazing advisor... something I discovered when trying to finish my PhD and he decided to take a sabbatical to Kenya to do missionary work :(.
I got enough statistics and machine learning experience in graduate school to be doing machine learning research now as part of the defense industry. Even if machine learning seems like it might be too hot to touch, it will aid you in any number of jobs you could have in the future: automation, data science, etc. It really is perhaps the easiest sub field to cross disciplines. I've always been a PC gamer and the TD-Gammon paper really pushed me forward in my desire to do machine learning -- check it out, its a classic (https://www.bkgm.com/articles/tesauro/tdl.html).
Oh wow TD Gammon was also partially responsible for inspiring me into a machine learning -> data science direction :) My dad brought me up playing backgammon so I had a fair understanding of all the heuristics involved - it was amazing to see a computer could learn them fairly well by itself.
True about switching fields with ml, after a while in evolvable hardware I did a joint PhD in geography + computer science, and have been making socioeconomic simulations of various kinds for the last 10 years. Nowadays I'm trying to reacquaint myself with core cs research, as looking for a lectureship I realized I'm always going to be stronger teaching cs than geography.
It's been a while since I was in that field, but I suspect those kind of low level operations are now heavily optimized in faster languages as sequences to operate on became longer and operations more complex.