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Postdoctoral Research Associate - Many-body Methods Applied to Correlated and Topological Materials

Date: May 14, 2022

Location: Oak Ridge, TN, US, 37830

Company: Oak Ridge National Laboratory

Requisition Id 7436 

Overview: 

Oak Ridge National Laboratory is the largest US Department of Energy science and energy laboratory, conducting basic and applied research to deliver transformative solutions to compelling problems in energy and security.

 

We are seeking a Postdoctoral Research Associate who will support the Materials Theory Group in the Materials Science and Technology Division (MSTD), Physical Sciences Directorate (PSD) at Oak Ridge National Laboratory (ORNL) who will apply or/and develop many-body quantum Monte Carlo (QMC) methods to explore the properties of correlated or/and topological materials. This is a synergistic effort involving an Advance Materials Theory field work proposal (FWP) and  the Center for Predictive Simulation of Functional Materials, a DOE-funded Computational Materials Sciences (CMS) Center (https://cpsfm.ornl.gov). 

 

The goals of our synergistic efforts are i) to understand challenging and intriguing materials with the most advanced methods, ii) develop and deploy advanced, systematically improvable quantum Monte techniques.  Our efforts aim to demonstrate and develop a new capacity of QMC techniques to conclusively predict, and hence ultimately design, the properties of correlated, topological, and quantum materials where understanding competing effects is of great importance.  Computational research will be engaged closely with experimental studies.  The available funding for this position can flexibly accommodate candidates with preferences on computational implementations or applications. Successful applicants will have many opportunities for collaboration with computational theorists and experimentalists across multiple national laboratories, including Oak Ridge, Argonne, Sandia, and Lawrence Livermore national laboratories. 

 

The ideal candidate will have detailed knowledge of quantum mechanics, statistical methods and experience in the application of computational electronic structure methods to correlated materials and experience with algorithm and code development.

 

Major Duties/Responsibilities: 

  • Learn/deploy many-body quantum Monte Carlo methods, such as diffusion and auxiliary field Monte Carlo
  • Perform quantum Monte Carlo calculations on high performance computers with the QMCPACK code to investigate the many body electronic properties of functional and topological materials
  • Perform initial density functional theory studies with codes such as Quantum Espresso
  • Plan and effectively execute computational research projects and perform detailed analysis of data in the context of quantum mechanics
  • Present and report research results at group meetings and international conferences
  • Publish scientific results in peer-reviewed journals in a timely manner
  • Ensure compliance with environment, safety, health and quality program requirements
  • Maintain strong dedication to the implementation and perpetuation of values and ethics

 

Basic Qualifications:

  • A PhD in Materials Science, Computer Science, or a related field completed within the last 5 years
  • Demonstrated experience in first-principles calculations in density functional theory, and beyond DFT methods such  quantum chemistry, dynamical mean field theory, GW, or quantum Monte Carlo

 

Preferred Qualifications:

  • An excellent record of productive and creative research demonstrated by publications in peer-reviewed journals
  • Experience developing electronic structure methods, and contributing to or releasing open source codes
  • Programming experience with Python
  • Experience working in Linux computing environments, especially in the context of high performance computers
  • Excellent written and oral communication skills
  • Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory 
  • Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs

 

Please submit three letters of reference when applying to this position. You can upload these directly to your application or have them sent to postdocrecruitment@ornl.gov with the position title and number referenced in the subject line.

 

Instructions to upload documents to your candidate profile:

  • Login to your account via jobs.ornl.gov
  • View Profile
  • Under the My Documents section, select Add a Document

 

Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be for up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and the availability of funding.

 

Moving can be overwhelming and expensive. UT-Battelle offers a generous relocation package to ease the transition process. Domestic and international relocation assistance is available for certain positions. If invited to interview, be sure to ask your Recruiter (Talent Acquisition Partner) for details.


For more information about our benefits, working here, and living here, visit the “About” tab at jobs.ornl.gov.

 

 

 

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.


If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.


ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply.  UT-Battelle is an E-Verify employer.


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