Postdoctoral Research Associate - Condensed Matter Theory

About the role

We are seeking a Postdoctoral Research Associate who will develop and apply computational methods based on electronic structure theory and artificial intelligence approaches with emphasis on electronic properties of a range of quantum materials important to the DOE mission, including the materials classes described below. Research efforts will include application of Quantum Monte Carlo (QMCPACK, PYQMC) density functional theory (e.g. QE, VASP, PYSCF) and associated models to describe various properties of DOE-relevant quantum materials.

Major Duties/Responsibilities

• Develop and use first principles methods to describe electronic and magnetic structure, excitations and interactions, phase stability, in a range of quantum materials
• Responsible for presenting and reporting key scientific results and publishing high-quality research in peer-reviewed journals
• Maintain strong commitment to the implementation and perpetuation of ORNL core values and ethics
• Postdoctoral research associates are required to work onsite at ORNL’s campus.
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success.

Basic Qualifications

• A PhD related to computational or theoretical condensed matter physics, theoretical chemistry, theoretical materials science, or other closely related field completed within the last 5 years
• Experience with computational modeling of electronic and magnetic properties

Preferred Qualifications

• An excellent record of productive and creative research demonstrated by publications in peer-reviewed journals
• Excellent written and oral communication skills with ability to communicate in English to an international scientific audience
• Scientifically curious and self-motivated with the ability to participate creatively in a collaborative environment
• Demonstrated experience with electronic structure methods including Quantum Monte Carlo or other many-body ab-initio methods for description of electronic, magnetic, and vibrational properties in a range of materials
• Expertise with artificial intelligence and machine learning approaches will be also considered