I have read about many astounding career paths of scientists in the US. E.g. someone had a BSc in education and switched to physics in graduate school. How are such switches possible?

In Europe I guess it's mostly so that such diverse careers can't be pursued without having had a reasonable exposure to the subject in college. One would have too many courses that are missing without prior knowledge so that they can't be put all in a Master's curriculum; thus, having had a specific subject is necessary as a major or minor in college...at least in Switzerland.

How come that in the US such switches are possible? Or is it just that all necessary courses are put into the graduate(+PhD) program lasting in total almost a decade, in which case I wouldn't wonder that it's possible?


My PhD was in neuroscience. When performing neuroscience research, there's a lot one needs to know:

  • Biology: Starting with the basics, almost any course in neuroscience will have both macro- and microbiology of the human/animal bodies being studies. This includes topics at the molecular, cellular, and systems level. There's a non-insignificant chunk of anatomy to be learned as well. A specific focus will be spent on the biochemistry of neural transmissions, which involves a lot of...
  • Physics: The principles of electromagnetism govern the firing of neurons. As such, a thorough understanding of both the chemistry and the physics leads to understanding neurons. To study these cells, people using implanted electrodes need to understand a fair chunk of circuitry, as the quality of their cellular recordings is dependent on them understanding impedance and amplification and such like that. People using surface monitoring (EEG/MEG) need to understand the basics of electromagnetism, which govern how the fields propagate and are measured. As an added bonus, they get to familiarize themselves with the signal processing of source localization as well. Wheee.
  • Psychology/Sociology Some people study how the brain controls complex behavior, such as motivation, learning, addiction, planning, visual abilities, motor abilities, bla bla and bla. This works for both animals and humans, so there can be a lot of literature to cover.

This doesn't begin to discuss the computational skills required to create and analyze experiments, or the statistical knowledge required to make sure you're not screwing up your analysis, or the writing (yes, that's a skill) knowledge required to write a paper that someone gives a poop about, or the public speaking skills required to give a conference talk.

And that's just neuroscience.

Long story short, successful researchers nowadays BECOME polyglot experts simply due to the highly cross-disciplinary nature of the fields themselves. Obviously, you don't need to be expert in all these fields, but you have to start somewhere. When it's all said and done, the person coming to neuroscience with a biology background is surprisingly not that far behind the person with a computational background, because they both have so much to learn.

  • Somehow, this answer doesn't seem to solve the riddle. Yes, no doubt, many fields are very interdisciplinary, and lots of learning about other fields is required for being successful in many cases. Yet ... I'm not sure how relevant that is for, say, Master studies after Bachelor studies in a different field. The skills required in such Master studies are often very specific and far away from what self-taught "career-changers" typically learn. To concretely outline my impressions: In CS, I frequently work with people from other fields who have migrated to CS. In many cases, these are ... – O. R. Mapper Apr 4 '16 at 19:28
  • ... physicists or mathematicians, but it also includes other fields. They bring in their own expertise from their fields, and of course, they usually know how to program some scripts or also moderately-sized applications. For the prototypes developed for research purposes, this is completely sufficient. But in terms of maintainability or systems architecture, the results are pretty much failures and couldn't live up to what is, in some cases, expected during CS Master's courses. The same applies to other things such as standard notations, commonly used data structures, design principles, ... – O. R. Mapper Apr 4 '16 at 19:29
  • ..., theoretical standard models, etc. And certainly, this is not specific to CS, but CS people switching to physics would have similar gaps in their knowledge. Of course, all of this can be learned with sufficient effort - but then, this can be said of almost everything. Advanced programs do not usually start at zero, and diversity of prior experience cannot realistically compensate for lacking specific knowledge from the field at hand, unless the follow-up career really is in a research-like environment with a considerable degree of self-direction rather than many pre-defined requirements. – O. R. Mapper Apr 4 '16 at 19:29

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