Picking up where I left off with blogging last year – this is part two of a write-up of a talk I’ve given a few times last year, part one is here. After talking about algorithms which deal with data that is not fully labeled, in this part I discuss how career choices can be similar, with my own as an example.
PhD
Doing a PhD was not on my radar until my MSc supervisor suggested that I apply for a position in the group. I liked the group and doing research for 4 years seemed like a good job to me (see my post on being an employee during your PhD). I didn’t have any specific long-term goal and, as I now realize, was clueless about most aspects of academia.
What I did understand is that it was good to publish papers. I had a few interesting (though not spectacular) results fairly early on, so I wrote papers and sent them to various workshops. I enjoyed these workshops a lot – since there were not that many people, I could meet researchers I’d just been citing, and have good discussions. On the other hand, I spent quite a lot of time writing smaller papers and pushing away the fact that I needed journal publications to graduate. Also, as I discovered later, my grant reviewers have never heard of these workshops, and thus were not impressed with my publication record.
I also did a lot of service and outreach activities. I had already been doing this type of thing as a student, so I was good at it, I enjoyed helping others, and it was good for my CV, whether I’d stay in academia or not. So I spent time organizing workshops, reviewing papers, giving talks to encourage more girls into science. I did learn something from all of these activities but in retrospect I think I spent a disproportional amount of time on them.
Postdoc
I doubted a lot before deciding to go for a postdoc. The awareness of the struggle of finding a position after, and all the people telling me I really have to go abroad to have any shot at it, didn’t help. In the end by talking to more mentors, I decided to go give it a try – without leaving the country.
My plan was to only do one postdoc and then get an independent position – or leave academia. As I understood to achieve an independent position I needed to do three things: publish on the project I was hired on, develop my own line of research, and get my own funding. I was not prepared to deal with so many different objectives, so in the end, I did all the things poorly. On top of that, I failed to take care of myself, and had to take a few months off to recover.
It was during a particular low point during the postdoc that I started blogging and tweeting more. It started with me publishing my CV of Failures – I thought I would be documenting a story that would end with me leaving academia. The response was overwhelmingly positive, and I continued with the How I Fail series. During all of this I found an incredibly supportive Twitter community, with many others who were going through similar struggles, and it’s been helpful ever since.
Tenure Track
Much to my surprise (and other feelings), I did find myself in a tenure track position after all. This is an important accomplishment, but at the same time, the next goal – getting tenure – is coming up in a few years. Again, there is this (self-imposed?) pressure to do all the things, so it is not without challenges. But, it is a much better experience in several aspects, because I occasionally realize that I don’t have to do all the things all the time. :
- I occasionally realize that I don’t have to do all the things all the time. I’ve now actually been able to have periods on time focusing on writing, then focusing on teaching etc.
- I occasionally (not often enough) realize that I don’t have to repeat the career paths on others to “succeed”. The combination of things that I do, might just be “good enough”, even though it doesn’t fit the typical “successful” CV.
- I have a lot of people, online and offline, who share or have shared many of the same experiences, and who have advice, or are just up for having a coffee or a beer when things are tough.
Academia as supervised learning
Where does the not-so-supervised learning come in? It seems to me that a lot of advice of what we need to do to “succeed” is based on rules derived from previous “successful” CVs – publishing at particular venues, doing a postdoc abroad, etc. Some of these rules we are explicitly told as advice, others we assume ourselves.
But there is a lot of missing from this picture. The “success” label is a function of much more than particular activities, but also the state of the world (number of tenure track positions, number of students, etc), and the state you are in yourself (including anything else you have to deal with next to the job search). These features have not been taken into account when creating the rules. So even if you do follow all the rules you might get a disappointing outcome, and vice versa.
There might also be opportunities that didn’t exist before. For example, few full professors would have been using Twitter during their PhDs and postdocs. As a yet “unlabeled” activity, it probably wouldn’t come up in any rules, but it can be a powerful tool for early career researchers.
Last but not least, it’s important to remember there’s more than one success metric, and why I’ve been writing “success” in a CV sense. Ultimately success should probably involve being happy, which can be achieved through other types of jobs. And perhaps some of these jobs are not even in our dataset yet.
Thank you for a very good post! Even for someone outside academia this is very true, CV’s come in very different forms and “success” isn’t very well-defined. I haven’t followed the traditional surveying/geo-informatics career path and am very difficult to place for managers abd recruiters.I have yet to find my dream job if such a thing even exists.
Thanks! I guess it’s true this holds outside academia too, I just have less experience with it 🙂 But for what it’s worth, I haven’t met many people who say they have their dream job (and if they do, I am not sure if it’s true)