What is a typical day like for a theoretical researcher?

[PabloAMC]

I think this thread is being really useful. Just let me point out two things that might be of your interest, Punck. First, acording to a paper I found at scholar google, the main programming languages at ATLAS (LHC) are currently: 1. C++, 2. Python, 3. FORTRAN. (I think you will have to trust my memory on this, I don't remember where I read it). You may want to learn any of those. FORTRAN is the oldest, pretty simple. Python is very simple to learn as well, you have millions of libraries all around and I think that is, with R, one of the most used languages in data mining and this kind of stuff. And finally C++ is more complicated but one of the fastest.
And the other thing you may want to know. There are in fact some libraries of programming called Computer Algebra System, that are sometimes used for symbolic programing. For example, if you want to do an analytical integral, they might be able to do it

 

[Plunck]

Hello Pablo, thank you for your advice.
I have already started learning some Python and a while ago I taught myself the basis of C ++. Again, as above, I don't love computers, but being aware of their importance I do brush up my lousy programming skills from time to time.

Punck

This isn't meant as a subtle insult, is it? ;)

 

[PabloAMC]

This isn't meant as a subtle insult, is it? ;)

Well, no it isn't, hehehe. Just typo, sorry

 

[Asteropaeus]

I am working in computational biology right now. The work is done almost exclusively my physicists or mathematicians.

People basically sit behind their computer most of the day. Writing code or reading papers with equations all over the place.
Then sometimes you write your own notes/paper. You need to be really smart to make a breakthrough in the math. It is mostly about applying stuff thought out by the truly brilliant to new problems. For the large part we use equations that have been thought of a long time ago, but only now we can use on biological systems. Either because how the field has developed, how much cheaper computational time has become, or how stuff can be compared to practical experiments that are possible or easy today, but were not in the past.

Every few days you spend an hour or so listening to someone explaining something difficult, or you explaining something difficult to someone else.
Or you discuss the problems and possible solutions with colleagues or supervisors/professors.

Sitting behind a computer sucks. You all sit there next to each other, barely talking. People lighten up in lunch breaks, though.

But practical work has it's own problems. You are sometimes in a basement of a building, all alone, repeating the same measurement over and over. You basically sit there and wait while it measures. Or you set it up and walk away to read papers.
Or, you have to wait for centrifugation steps. Sometimes waiting steps are too short for you to do something else, but long enough to get really annoying.
I have had professors tell me this is the exact reason why they choose to do theoretical work. They hate spending so much of their time waiting or doing the same mundane things over and over.

Getting real results is really slow in any field. Big areas of disappointment and short moments of euphoria. It is hard.

 

[Gullik]

Sitting behind a computer sucks. You all sit there next to each other, barely talking. People lighten up in lunch breaks, though.

I can't really say that this reflects my own experience working on computer stuff. (Co-developing a new parallel PiC (particle-in-cell, plasma) program)
We easily spend a third of the 'coding time (when we are productive) discussing problems, algorithms, alternative algorithms, test scenarious and results, coding practices.
I generally think that two developers that stops often and discusses problems with each other get much more done than if they each sit quietly coding alone.

 

[Asteropaeus]

I guess there is some hyperbole and maybe it's just more true for me currently because of circumstances.

 

[Crass_Oscillator]

Biology tends towards being incredibly applied, as there is no strong theoretical foundation on which to base one's algorithms. Brute force and intuition currently outmatch cleverness and sophistication as a general rule.

However there are many other fields where the theoretical foundation is surer and so more pencil and paper mathematics accompanies the raw computer stuff.