Timeline for Publication and Career as a fresh Ph.D
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 27, 2012 at 14:27 | comment | added | Yemon Choi | I second Eric's comments, with the added note of urgency: it can take a while for these things to get refereed, accepted and published. My own publication record might look to a non-mathematician like I was on fire in 2010, just because results I wrote up in 2007 took 3 years to see official publication. | |
| Oct 18, 2012 at 3:40 | comment | added | Colin Reid | Following on from Eric's comment, I wish more 'big machines' would get published as such. You can write a longish paper in two parts: part one sets up the machine, and part two spits out a whole bunch of results with minimal extra effort. It doesn't matter if the payoff results individually have shorter proofs that bypass the machine - you're just demonstrating what the machine can do in order to sell it to the mathematical community. If they buy it, they'll streamline it and use it to prove their own results, and suddenly your work is the centre of attention. | |
| Oct 18, 2012 at 3:15 | comment | added | Eric Katz | Just a thought: it's often hard to judge the strength of your own results. Often while writing a dissertation, one has a lot of ideas that they can't fit in or don't have time to work on but which seem easy because he/she has developed a big machine. These seem less easy to people who have not written that same dissertation. I recommend to recent Ph.D.'s that they turn those ideas into papers. Chances are that they are going to make lots of mistakes in trying to publish their results and may have a turnaround as long as a year on papers. So it's worth writing some small papers early on. | |
| Oct 17, 2012 at 21:07 | comment | added | Henry Cohn | Definitely, it's better to gather this information (while keeping in mind that there's a lot of noise). | |
| Oct 17, 2012 at 21:01 | comment | added | Andy Putman | I agree that it is hard to make precise comparisons. However, it gives you a rough idea of what is needed. In my experience talking to postdocs, lots of them are totally clueless about what the CV's of job candidates at (say) top 50 schools look like. Moreover, faculty are often somewhat clueless and loathe to deliver bad news. Another good source of recent info is the math jobs rumor wiki. It is very educational to look at the cv's of people on the various short lists (with the understanding that many of those lists are wrong). | |
| Oct 17, 2012 at 20:49 | comment | added | Henry Cohn | This is all good advice, but #4 can be tricky. One reason is that it's hard to reconstruct what someone's job application might have looked like, because of issues like how far before publication they were circulating preprints. However, there are deeper ambiguities in comparing CVs. You'll often see a case where X had twice as many papers as Y in roughly comparable journals. Were Y's papers judged more impressive by experts? Was Y just lucky? Did X face a tougher year on the job market? Did X solve a two-body problem by turning down more prestigious offers? You generally won't know. | |
| Oct 17, 2012 at 20:28 | history | answered | Andy Putman | CC BY-SA 3.0 |