Postdoctoral Research Associate- Computational Chemist in Data Science

Oak Ridge National Laboratory; Oak Ridge (TN); On-site; USD 60,000 - 90,000; Full time

Job summary

An established industry player is seeking a Postdoctoral Research Associate to advance computational chemical and materials science through innovative machine learning and high-performance computing techniques. This role involves working on cutting-edge research in ligand binding and molecular simulations, contributing to a multidisciplinary team. The successful candidate will utilize advanced computational methods to predict materials properties and design new ligands, driving scientific breakthroughs in the field. Join a dynamic environment that values diverse perspectives and fosters collaboration, making a significant impact on energy and environmental challenges.

Benefits

Medical and Retirement Plans

Flexible Work Hours

On-site Fitness Facilities

Employee Discounts

Generous Vacation and Holidays

Educational Assistance

Relocation Assistance

Qualifications

• Ph.D. completed within the last 5 years in a related field.
• Experience with AI and ML tools for materials property prediction.

Responsibilities

• Conduct computational studies using machine learning tools.
• Develop machine-learned interatomic potentials for modeling.

Skills

Machine Learning

Deep Learning

High-Performance Computing (HPC)

Molecular Dynamics Simulations

Density Functional Theory

Programming for Scientific Computing

Education

Ph.D. in Theoretical or Computational Chemistry

Tools

Machine Learning Tools

Computational Chemistry Software

Job description

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Postdoctoral Research Associate- Computational Chemist in Data Science

We are seeking a Postdoctoral Research Associate who will focus on delivering groundbreaking computational chemical and materials science at the forefront of the field through the development and use of machine learning, deep learning, and high-performance computing (HPC). This position resides in the Chemical Separations Group in the Separations and Polymer Section, Chemical Sciences Division, Physical Sciences Directorate, at Oak Ridge National Laboratory (ORNL).

In this role, you will perform computational studies using machine learning tools aimed at better understanding of ligand binding in solution and molten salts chemistry. Computational methods will primarily involve density functional theory calculations for molecular and periodic systems, molecular dynamics simulations using ab initio and machine-learning potentials, and the development or application of machine-learning tools for feature extraction, property prediction, and inverse molecular design. You will also work within a multidisciplinary multi-institutional team involving specialists in theory, spectroscopy, organic synthesis, and surface characterization.

Major Duties/Responsibilities:

• Work with a unique team of scientists seeking to advance scientific understanding of structural and energetic aspects of ligand binding to target ions in bulk aqueous and nonaqueous environments.
• Construct machine-learning models for feature-based molecular property prediction and drive the inverse design of ligands with engineered properties.
• Develop machine-learned interatomic potentials trained on ab initio data to accurately model the thermodynamic and thermophysical properties of complex materials.
• Conduct molecular simulations to elucidate the thermodynamic and structural basis of enhanced binding and selectivity manifested in improved separations using the leadership class high performance computing facilities available at ORNL and other DOE facilities.
• Plan and conduct simulations and work with experimentalists to help interpret spectroscopic results and guide the design of new chelating agents.
• Participate in project planning and execution.
• Present and report research results and publish scientific results in peer-reviewed journals in a timely manner.
• 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 Ph.D. in theoretical or computational chemistry or closely related field in physical chemistry or materials science completed within the last 5 years.
• Experience in applying artificial intelligence and machine learning tools for materials property prediction and materials design.
• Experience in employing molecular simulations for predictive modeling of materials properties.

Preferred Qualifications:

• Demonstrated success in predicting materials properties and discovering new functional materials.
• Experience applying ML approaches for feature extraction and property prediction and developing generative models for inverse design of new materials and ligands for targeted separations.
• Experience in developing and utilization of machine learning interatomic potentials (MLIP) to enhance predictive modeling of chemical processes and materials.
• Programming experience for workflow development and scientific computing.
• 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.

Special Requirements:

Postdocs:

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 up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.

Letters of Recommendation:

Please submit three letters of reference when applying to this position. You can upload these directly to your application or have them sent to [e-mail] 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

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: [e-mail].

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.