Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
Semester 1 - Dual-Delivery
Semester 2 - Dual-Delivery
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The subject offers a range of projects in modules that offer experience in laboratory techniques and computational methods; the relative weights are indicated in the module descriptions. Students must select four projects with a combined weighting that contains at least 25% Computational Physics and 25% Laboratory Physics. The laboratory projects include nuclear physics, particle physics, diffraction, electronics, atomic physics, optical physics and astronomy. The computational projects are designed to develop programming skills and to introduce a range of numerical methods commonly used in physics research will be based on model problems in physics; these may include electronic structure theory, molecular vibrations, stellar structure, quantum spin systems, large-scale magnetic systems and gravitational lensing by point masses. Some projects may be offered that merge laboratory and computational work with approximately equal weighting.
Intended learning outcomes
This subject challenges students to expand their knowledge of fundamental physics principles and develop their capacity to:
- demonstrate an understanding of a wide variety of advanced experimental and data analysis techniques;
- acquire, analyse and interpret experimental data; and
- write and evaluate scientific and technical reports.
- explain the application of a variety of computational techniques including differencing, root finding, quadrature, ordinary and partial differential equations, matrix eigenvalue problems, Monte Carlo methods, fast Fourier transforms and data processing algorithms to physical problems; and
- apply these methods to a range of physical situations and to experimental data.
A student who completes this subject should be able to:
- explain the application of a variety of computational techniques including differencing, root finding, quadrature, ordinary and partial differential equations, matrix eigenvalue problems, Monte Carlo methods, fast Fourier transforms and data analysis algorithms to physical problems
- apply these methods to a range of physical situations.
- acquire and interpret experimental data and design experimental investigations
- participate as an effective member of a laboratory group.
- think independently and analytically, and direct his or her own learning
- manage time effectively in order to submit assessable work when required.
Last updated: 8 May 2021