Postdoctoral Fellow

Tulane University • New Orleans, LA

2d • Hybrid

About The Position

The Physics and Engineering Physics Department at Tulane University and the Quantum and Condensed Matter Physics Group in the Theoretical Division of Los Alamos National Laboratory (LANL) have an immediate opening of a postdoctoral position in the area of computational materials physics. The postdoc will focus on applying first-principles density functional/time-dependent density functional theory (DFT, TDDFT) and high-throughput approaches in combination with machine learning techniques for discovery and inverse design of novel 2D d-electron materials, including those with non-trivial topological properties. The work will be a combination of development and implementation of TDDFT models for magnonic excitations, development of computational workflows, and applying existing DFT codes. Successful applicants will join a highly collaborative project aimed at unraveling the fundamental properties of these systems, establishing a new route to quantum information, and laying the foundation for future quantum information devices, and will be an integral part of a dynamic team of condensed matter theory and experimental scientists at Tulane and LANL. The position is renewable for up to four years. The postdoc will be based at Tulane and will make two two-week visits per year to LANL.

Requirements

• Strong skills in condensed matter theory/computational materials theory.
• Excellent scientific record of publications.
• Expertise in first-principles density functional theory simulations.
• Expertise with TDDFT or other many-body theory.
• Ability to work creatively as a part of a diverse team and independently.
• Strong written and oral communication skills.
• Ph.D. in Physics, Chemistry, or Materials Science (preferably completed within the last 5 years).

Nice To Haves

• Knowledge of machine learning.
• Programming skills in either Python, C++, or Fortran.
• Experience working in Linux computing environments, especially in the context of high-performance computing.
• Working experience with standard DFT/TDDFT methods, i.e. VASP, Yambo.

Responsibilities

• Applying first-principles density functional/time-dependent density functional theory (DFT, TDDFT) and high-throughput approaches in combination with machine learning techniques for discovery and inverse design of novel 2D d-electron materials.
• Development and implementation of TDDFT models for magnonic excitations.
• Development of computational workflows.
• Applying existing DFT codes.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree