Recruiting Two Ph.D. Students

Created on March 14, 2025

2025

Two Fully Funded PhD Positions in First-Principles Simulations

Starting date: Sep. 2025

Number of Positions: 2

Scholarship: MOP 10,000/month (36 months)

Last application date: May 2025 (recruiting will close once positions are filled)

Research Area 1: Dynamical Mean-Field Theory (DMFT) Study of Strongly Correlated Systems

This research focuses on investigating the fundamental electronic structure and magnetic correlations of strongly correlated systems using the DFT+DMFT method. Topics include, but are not limited to: Spin liquids, Berezinskii-Kosterlitz-Thouless (BKT) phase transitions, Magnon-phonon coupling, ultrafast magnetization dynamics (SOT, STT), Spin waves, X-ray absorption spectroscopy calculations, etc.

Requirements:

  1. A master’s degree in physics, materials science, or a related field before enrollment.
  2. Familiarity with density functional theory (DFT) and at least one related software package, such as VASP, WIEN2k, Quantum Espresso.
  3. At least one first-author publication in a relevant field.
  4. Experience in model Hamiltonian calculations is preferred.
  5. Experience with Wannier90 and Fortran programming is an advantage.

Research Area 2: Deep Learning-Assisted Material Property Prediction

This project aims to develop an accurate and efficient deep learning framework for predicting the fundamental properties of strongly correlated systems, including magnetic properties (exchange parameters, magnetic phase transitions, exotic spin texture), electronic structures, superconductivity, and more.

Requirements:

  1. A master’s degree in physics, computer science, materials science, or a related field before enrollment.
  2. At least one first-author publication in a relevant field.
  3. Familiarity with deep learning algorithms such as graph neural networks (GNNs), convolutional neural networks (CNNs), equivariant neural networks (ENNs), and proficiency in Python programming.

Interested candidates are encouraged to send their CV to duo.wang@mpu.edu.mo, along with a brief self-introduction and their interested research area.