Background: computational condensed matter physics

[teame Yitbarek]

I have my MSc in 'Computational condensed matter physics'. I used VASP package for simulation during my MSc. and i am also well experienced in FORTRAN programming language. Can anyone give me short note about 'PhD in computational physics'? so that can continue my PhD in 'Computational Physics'.

 

[teame Yitbarek]

Summary:: Interested in doing 'PhD in Computational Physics' .

I want to discuss about computational physics for PhD. i have an experience in FORTRAN, vasp. what example titles or areas of study are there in computational physics (PhD)?

 

[mpresic3]

Do not restrict yourself to computational physics. Many researchers in astrophysics that I know did much of their work running models written in FORTRAN. I think the same could be said for fluid dynamics, theoretical nuclear physics, geodesy, chaos, perhaps numerical relativity, and mathematics. You may be able to approach a research advisor or an employer more broadly. For example, if your MS was solving a transport problem in solid state physics, you may find a nuclear physics position interesting, in that a similar transport problem. Nucleons move, and stars move too. The downside is you will have to develop expertise in another area.

In short, I think you may have a hard time finding a position in doing computational physics alone, if you mean solely developing algorithms to calculate physical quantities easily. You may need to find a allied field to apply the computational physics to.

Many so called "modern" programmers are pushing python right now.

However, a lot of earlier (legacy) code is in FORTRAN. I find organizations need (unfortunately they do not always value as much as I think they should) scientists that can program and know FORTRAN enough to understand what the old code did? Why the software was important, and how it can be used effectively in the future? Without knowing these answers, rewriting the code in a "modern" language is pointless.

 

[Alex Petrosyan]

I second that you should under no circumstances limit yourself to Computation. I myself have done my masters on nested sampling (Python and very little fortran). For Condensed matter, I would suggest you look into DFT (there's a lot of mathematics that lends well to Computation), and some abstract topics. I would suggest you put the what before the how and computation is pretty much the how.