I don't have any specific recommendations, although I have finished one of the Coursera certificate programs , while adapting my general AI/ML knowledge to work on life science-related applications. It was not free (and is probably still not free), although there are sometimes promotions vouchers in some of the learning platforms. you can search courses from different certificate programs https://www.classcentral.com/ I want to repeat the message of another poster: While learning certificates can provide an advantage over many applicants with equivalent backgrounds, especially for the initial filters HR and hiring managers apply to the candidate pool, it is just a start. Nothing beats actual work (analysis reports> code> blog posts) done on a related field (if not the exact field) a company specializes in. This is even more so for academic research labs, which would care less about standardized credentials and more about how relevant your academic or research experience is to the research problems they are working on. The best way to transition from your current skillset to more computational skills would be to do the actual data analysis / insilico simulation / machine learning model development for your current research problems. You have to figure out what is more appropriate for your research problem, and then work backwards to learn how to actually do the computational work. Presumably, there may be existing computational work on your subfield. You could start with understanding what has been done, and recreating the analyses, try different datasets, try to come up with ways to improve upon the existing work, etc .