Non-Funded Studentships

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Machine learning in statistical physics Non-funded

Centre for Fluid and Complex Systems | Engineering, Environment and Computing

The student will use deep neural networks to quantitatively describe phase transitions in pure and disordered spin systems. Advanced techniques such as GANs will be employed to directly sample configurations of such systems. As a result, the student will be able to map out the utility of such methods for research in statistical physics and potentially open up exciting new avenues resulting from combining approaches from different disciplines.