Requisition Id 16540
Overview:
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.
The Materials Theory Group has a background in using the most advanced ab-initio methods to examine electronic, topological and magnetic properties of advanced materials, their defects and their interphases. The incumbent will have opportunities to develop research methodologies as well as collaborate with experimental groups at Oak Ridge National Laboratory (ORNL). This position resides in the Materials Theory Group in the Materials Science and Technology Division (MSTD), Physical Sciences Directorate (PSD) at ORNL.
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
Special Requirements:
Security, Credentialing, and Eligibility Requirements:
For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required. Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.
To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.
For foreign national candidates:
If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment. Once you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment.
Letters of Recommendation:
Please submit three letters of reference when applying to this position. You may upload these directly to your application or have them sent to psdrecruit@ornl.gov.
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
About ORNL:
As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.
ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience.
Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts.
If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: ORNLRecruiting@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.
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.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The emergence of Postdoctoral Research Associates in Condensed Matter Theory signifies a critical investment in the foundational layer of the quantum materials value chain. This role type is structurally necessary to bridge the gap between fundamental electronic structure theory and the realization of scalable quantum technologies. By leveraging advanced computational methodologies, these researchers address the "design gap" in materials science, ensuring that theoretical breakthroughs in phase stability and magnetic structure are converted into viable pathways for national security and energy applications. Market signals from global technology assessments highlight that such expertise is essential for mitigating risks associated with the materials-level bottlenecks currently limiting hardware performance. This function serves as a primary mechanism for accelerating the development of next-generation quantum bits and sensors within high-performance computing ecosystems.
The theoretical condensed matter landscape is transitioning from descriptive modeling to predictive design, driven by the convergence of electronic structure theory and artificial intelligence. In the broader quantum ecosystem, this role type sits at the critical interface of fundamental research and application enablement. While hardware modalities proliferate, the primary constraint remains the discovery of materials with robust topological and magnetic properties. Sector-wide efforts continue to address talent and integration challenges in quantum systems by embedding theoretical insights into the experimental pipeline, thereby reducing the high-capital risk of fabrication failure.
Macro-level dynamics are heavily influenced by public funding cycles and the strategic mandates of national laboratories. The current industry focus lies on bridging classical and quantum capabilities at scale, which necessitates the standardization of many-body ab-initio methods across disparate computing platforms. This structural layer of expertise is vital for maintaining the momentum of Technology Readiness Level (TRL) progression, as organizations shift from proof-of-concept discovery to the optimization of material interphases and defects. Workforce scarcity in this domain is particularly acute, as it requires a rare synthesis of many-body physics, high-performance computing (HPC) proficiency, and machine learning integration.
Furthermore, the evolution of the deep-tech value chain depends on the ability to translate complex materials science problems into deterministic technology roadmaps. As global competition for quantum supremacy intensifies, the role of the theoretical associate becomes a primary determinant of an organization’s ability to achieve "material advantage." By providing the architectural blueprints for quantum materials, these researchers ensure that the physical foundation of the ecosystem remains compatible with the rigorous demands of fault-tolerant computing and large-scale industrial deployment.
The capability architecture for this role type centers on the synchronization of first-principles methods with the protocols of modern data science. Mastery of advanced many-body techniques, such as Quantum Monte Carlo and Density Functional Theory (DFT), is essential for ensuring that material simulations accurately capture electronic excitations and interactions at the atomic scale. This requires deep integration with high-performance computing (HPC) environments to manage the massive data throughput of complex electronic structure calculations. These capabilities matter because they provide the predictive leverage needed to bypass the "trial-and-error" phase of experimental discovery, significantly increasing the throughput of the research cycle.
Interoperability with artificial intelligence and machine learning layers is increasingly critical for the reproducibility and scalability of materials modeling. This cross-functional coupling allows for the creation of surrogate models that can rapidly screen thousands of candidates for specific magnetic or topological traits. Such expertise is fundamental to the stability of the quantum software and hardware value chain, as it establishes the rigorous verification and validation frameworks necessary for long-term technology transitions. By bridging the software-hardware interface through predictive modeling, this function secures the interoperability of emerging quantum materials within existing scientific infrastructures.
• Accelerates the deterministic transition from fundamental theory to functional quantum materials
• Mitigates capital-intensive research risks by implementing high-fidelity predictive modeling
• Facilitates the integration of advanced electronic structure methods into high-performance computing workflows
• Strengthens the reliability of technology roadmaps through rigorous material phase stability analysis
• Reduces iteration friction between theoretical discovery and experimental material fabrication
• Optimizes the allocation of computational resources across complex many-body simulation portfolios
• Enhances the stability of the quantum supply chain by identifying alternative high-performance materials
• Supports the scaling of quantum sensors by providing architectural blueprints for magnetic excitations
• Improves the transparency of material readiness levels for national security and energy stakeholders
• Enables the structural reproducibility of quantum experiments through standardized simulation protocols
• Protects long-term research and development investments by ensuring scientific alignment with industrial scalability
• Orchestrates the convergence of academic theory with the practical demands of the Department of Energy mission
Industry Tags: Condensed Matter Theory, Quantum Materials, Electronic Structure, Quantum Monte Carlo, Density Functional Theory, Artificial Intelligence Integration, High-Performance Computing, Materials Science, Deep Tech Research
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