I am currently a third year PhD student in Neuroimaging. My project is divided evenly in the neurosciences and in medical imaging, specifically diffusion MRI, and is purely dry-lab. My work requires computational, programming, statistical and a bit of physics knowledge to effectively tackle research problems in this area. Before my PhD, my honours year involved drug discovery in the parasitology field. I changed from wet-lab to dry-lab after my Honours because I didn't like wet-lab research and was much happier crunching numbers on a computer.
I am very interested in making a switch to computational biology after my PhD because I love biological/clinical research problems and I would like to apply or develop my computational skills in the biology field, as opposed to imaging physics.
I have a few questions to ask from researchers/professionals in computational biology regarding this topic:
1. I understand that I will need to get up to speed with the computational skills needed in computational biology. I'm more interested in applying the skills to answer research/data problems, rather than developing software/algorithms without a research focus, so training wise, should I apply for formal qualifications after my PhD (i.e. a Masters in Biostatistics) or gain such knowledge through online training (i.e. Coursera and Rosalind)?
2. I am used to learning by doing the programming and statistics and teaching myself the computational skills needed for the problem. I do understand the argument that teaching yourself these computational skills is much more valuable and insightful than a University course, however, I would like to apply for computational biology Post Doc positions in the future. Given this aim, would a formal qualification be better in the long run than taking online courses (even with online certification)?
I've browsed this forum for topics similar to this one that I'm posting and I did find a topic with some great answers: (https://www.biostars.org/p/133865/), however I'd like some feedback into my own personal situation.