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> This is true if you need large amounts of resources ($) to achieve your research goals. My experience is that the quoted situation is the exception in US academia. Despite paying a pittance, graduate students are expensive enough to make professors dependent on the approval of funders. And that's where the loss of autonomy comes from. It seems to me that certain parts of academia that are more funded by teaching like pure math may be more immune to this, but STEM broadly seems accurately described by the chapter I recommended. > However, what I was thinking about when I wrote that was that I control my schedule, calendar, research objectives, and have a yearly check in on my performance. In contrast, my experience in industry was VERY different across the board. I agree with what you said, aside from that I don't think you have full autonomy over your research objectives. If you think you control your own research objectives, I recommend reading the chapter I mentioned. As far as I can tell, your interests are well aligned with what's supported. I looked you up, and was not surprised to see that you work in deep learning, one of the most supported and hyped fields around today. People (like myself) whose interests aren't aligned with what's supported often find academia to be quite hostile. The relative research autonomy you feel is real, but if you decide to move outside of deep learning, you may no longer find the same sense of research autonomy. |