R&D Associate Staff - Atomic manipulation with AI agents

Requisition Id 15730 

­­Overview:  

The Center for Nanophase Materials Sciences (CNMS) is seeking a Research & Development Associate Staff Scientist to support research directed towards developing novel AI agents that can be implemented on atomic scale microscopy platforms such as scanning tunneling microscopy (STM) and scanning transmission electron microscopy (STEM) as well as synthesis platforms such as pulsed laser deposition (PLD) to synthesize and optimize materials. The focus will be on developing and improving the reliability of agentic AI platforms that can perform atomic manipulation in STM or STEM but will also branch into thin film synthesis such as PLD.

As a Research Associate, you will contribute significantly to research in these areas, bridging simulations with modern AI agents (for example, using reinforcement learning) to inform policies to drive atomic manipulation in microscopy experiments leading to discovery and creation of novel states of matter. In addition to fundamental science discovery, the research will pursue development of data pipelines using automated workflows, creation of multi-modal databases and novel ML-approaches that allow integration of different theory, simulation, and multi-modal experimental protocols. The research is designed to provide opportunities for development of your experience and scientific vision. The position resides in the Data NanoAnalytics Group, Theory & Computation Section, Center for Nanophase Materials Sciences (CNMS), Physical Sciences Directorate (PSD) at ORNL.

Major Duties/Responsibilities: 

• Design, implement, and deploy advanced agent-based AI frameworks (e.g., reinforcement learning, hierarchical policies, multimodal agents) and apply them to scanning probe microscopy platforms—including STM and STEM—to autonomously manipulate atoms and construct designer lattices with targeted quantum or electronic properties.
• Extend AI-driven control approaches to thin-film synthesis platforms, such as pulsed laser deposition (PLD), by developing closed-loop optimization strategies for growth conditions, defect engineering, and in-situ diagnostics.
• Integrate simulations, theory, and experiment by developing workflows that combine atomistic modeling, surrogate models, and agent policies to guide experimental decision-making for materials discovery.
• Develop robust automation pipelines that orchestrate data acquisition, analysis, experimental control, and model retraining, enabling reproducible, scalable, and high-reliability AI-driven experimentation.
• Report and publish scientific results in peer-reviewed journals in a timely manner
• Present results at international scientific conferences and meetings
• 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 in Physics, Materials Science, Chemistry, Computer Science, or closely related field
• Sound understanding of advanced ML concepts and architectures and hands-on experience with open-source AI/ML packages (such as PyTorch, scikit-learn, TensorFlow, JAX etc.)
• Two or more years of experience in applying machine learning methods for instrument control, such as on a microscope, or on a nanomaterials synthesis platform resulting in publishable scientific results
• Two or more years of experience with state-of-the-art machine learning methods such as reinforcement learning and Bayesian optimization
• Experience with operating microscopy platforms (scanning probe or electron microscopy) and/or nanomaterial synthesis platforms (such as physical vapor deposition or molecular beam epitaxy)

Preferred Qualifications:

• Strong understanding of concepts in solid-state physics, ferroelectrics and/or 2D materials
• Experience with advanced AI/ML methodologies relevant to autonomous science, such as generative models, causal inference, symbolic regression, or model-based RL for scientific reasoning and materials design.
• An excellent record of productive and creative research shown by a record of publications in peer-reviewed journals, and
• Demonstrated coding abilities and a commitment to open science as shown through code repositories (e.g., GitHub)
• 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 changing needs.

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.

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.