We have an opening for a postdoctoral fellowship (2 years, fixed term) in theoretical and computational physics of ultracold few-atom systems. The postdoctoral fellow will work on fundamental aspects of quantum dynamics, semiclassical quantum chaos, and stochastic computational methods applied to strongly correlated ultra-cold atom systems with short-range interactions.
A further aspect of the project is the development and implementation of stochastic algorithms for quantum dynamics and excitation spectra of closed and open quantum systems, extending the existing projector quantum Monte Carlo algorithms in the software package Rimu.jl.
The position is located at Massey University’s campus in Auckland, New Zealand. New Zealand is currently free of covid (in the community) and a wonderful place to live and work. Due to the pandemic it has border restrictions in place and thus we are particularly encouraging applicants who either reside in New Zealand or are entitled to travel to New Zealand (NZ nationals or permanent residents). We will also consider international applicants who, however, will require a border exemption for travelling to NZ.
Applications close 1 September 2021 (NZ time zone!).
More job details on UCAN and on the Massey University Careers site.
Research group web site: Brand group.
Please ask here or PM me if you have any further questions!
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Rimu.jl is a nice quantum Monte Carlo code, although it is a little different from what I did which is path integral quantum Monte Carlo in nuclear physics. In this area it seems the code are now mostly using modern Fortran.
May I venture to ask an irrelevant question,
what is the main consideration when choosing Julia to write Rimu instead of using modern Fortran or C++?
Is it because somehow Rimu needs to solve differential equations?
Thanks for the question. Indeed, Rimu.jl is using a stochastic power method, which is quite different from path integral QMC.
There are a number of reasons why we chose to go with Julia. No, Rimu.jl does not rely on differential equation solvers. Many good reasons (as in this post) we only discovered later. At the time it was just the appeal of having a modern high-level language with easy-to-read syntax and access to plotting, scripting, etc, while being able to write fast and generic code. We wanted to keep the code flexible such that we could experiment easily with different algorithms, and different physics problems. We had the option of diving into an existing Fortran code base that was written and optimised for quantum chemistry, and extending it to do the things we wanted (bosons, mixtures with different spin flavours), which seemed really hard. Writing a new code in Julia seemed much more fun. And it was!
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