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Research Fellow (Computational Condensed Matter Physics)

2-5 Years
SGD 4,000 - 8,000 per month
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  • Posted 6 hours ago
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Job Description

The School of Physical and Mathematical Sciences (SPMS) at NTU Singapore hosts research and education activities in two divisions: Division of Mathematical Sciences (MAS) and Division of Physics and Applied Physics (PAP). MAS covers diverse topics ranging from pure mathematics to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and photonics. Over the years, SPMS has attracted talented individuals from around the world and Singapore to join as scientific leaders and researchers.

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Join the research group of Pinaki Sengupta at SPMS. We are seeking a Research Fellow to contribute to a project focused on using advanced numerical methods to explore low energy dynamics in strongly interacting quantum spin systems. The candidate will develop and implement advanced algorithms to investigate the nature of low energy excitations in integer spin systems in low dimensions and their evolution across a quantum phase transition. In particular, the candidate will focus on the spin nematic phase in two dimensions and the generalised Haldane phase in quasi one-dimensional systems.

Key Responsibilities:

  • Develop quantum Monte Carlo algorithm to simulate imaginary time dynamics in integer spin systems

  • Implement state of the art analytic continuation method to extract low energy spectrum from imaginary time dynamics

  • Calculate experimentally observable quantities and compare simulations with inelastic neutron scattering experiments of quasi 1D quantum magnets

Job Requirements:

  • Ph.D. in Physics or related field

  • Knowledge of quantum many body physics and quantum phase transitions

  • Expertise in quantum Monte Carlo and analytic continuation methods

We regret to inform that only shortlisted candidates will be notified.

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Job ID: 137374835