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Machine Learning Material Science Postdoc Jobs (NOW HIRING)

Virginia Tech

Postdoctoral Associate

Blacksburg, VA · On-site

... materials discovery. Responsibilities : • Contribute to building AI-native frameworks that ... modeling, machine-learning methods, knowledge-graph and ontology-based scientific data ...

LLNL

Machine Learning Researcher

Livermore, CA · On-site

... physics, material science, predictive medicine, and treatment discovery. You will also have the ... Adapt current machine learning research to real world applications at scale, with potentially ...

LLNL

Machine Learning Researcher

Livermore, CA · On-site

... physics, material science, predictive medicine, and treatment discovery. You will also have the ... Adapt current machine learning research to real world applications at scale, with potentially ...

LLNL

Machine Learning Researcher

Livermore, CA

... physics, material science, predictive medicine, and treatment discovery. You will also have the ... Adapt current machine learning research to real world applications at scale, with potentially ...

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Machine Learning Material Science Postdoc information

What is a machine learning material science postdoc?

A Machine Learning Material Science Postdoc is a researcher who applies advanced machine learning techniques to solve problems in material science. This role typically involves developing algorithms to predict material properties, optimize materials design, and analyze complex datasets generated from experiments or simulations. Postdocs in this field often work in interdisciplinary teams, collaborating with chemists, physicists, and engineers. Their research can accelerate the discovery of new materials for applications such as energy storage, electronics, and manufacturing.

What are the key skills and qualifications needed to thrive as a machine learning material science postdoc?

To thrive as a Machine Learning Material Science Postdoc, you need a doctoral degree in materials science, physics, chemistry, or a related field, along with a strong foundation in machine learning algorithms and data analysis. Familiarity with programming languages such as Python, machine learning libraries (e.g., TensorFlow, PyTorch), and materials simulation software is essential. Strong problem-solving abilities, collaboration skills, and effective scientific communication help you work across interdisciplinary teams and present research findings. These skills ensure innovative research, accurate modeling, and impactful contributions to the advancement of material science using AI.

How does a machine learning material science postdoc typically collaborate with experimental researchers in multidisciplinary teams?

As a Machine Learning Material Science Postdoc, you will frequently work alongside experimental scientists, chemists, and engineers. Collaboration often involves translating experimental data into machine learning models, suggesting new experiments based on predictive insights, and validating computational results with laboratory outcomes. Clear communication and interdisciplinary understanding are key, as you bridge the gap between computational modeling and practical material synthesis or characterization. This collaborative environment not only enhances research outcomes but also broadens your professional skill set.

What are popular job titles related to Machine Learning Material Science Postdoc jobs?

For Machine Learning Material Science Postdoc jobs, the most frequently searched job titles are:

Infographic showing various Machine Learning Material Science Postdoc job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution.

Research Associate in Materials Science and Engineering

Charlottesville, VA • On-site

University of Virginia
Colleges, Universities, and Professional Schools • 10K+ employees
Learn more about University of Virginiaexternal

$47K - $90K/yr

Full-time

Re-posted 28 days ago

University Of Virginia rating

7.9

Company rating: 7.9 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

Job description

The Ke Research Group in the Department of Materials Science and Engineering at the University of Virginia is seeking applicants for a Postdoctoral Research Associate position in computational materials science and theoretical condensed matter physics, with a focus on strongly correlated materials, especially rare-earth systems.
The successful candidate will work on fundamental research projects in rare-earth magnetic materials, including permanent magnets and quantum materials, with emphasis on analytical modeling, first-principles electronic-structure methods, rare-earth 4f physics, crystal-field effects, high-throughput materials discovery, and machine-learning-assisted materials design.
RESPONSIBILITIES:
  • Develop theoretical methods and computational approaches for strongly correlated rare-earth materials
  • Perform and analyze ab initio calculations for strongly correlated systems
  • Develop automated workflows for materials screening and property prediction
  • Collaborate with experimental and theoretical partners across groups and institutions to interpret results and guide materials discovery
  • Prepare manuscripts, present research results, and contribute to project reports and proposals
  • Mentor graduate and undergraduate students as appropriate

REQUIRED QUALIFICATIONS:
  • Ph.D. in physics, materials science, chemistry, or a related field by the start date
  • Research experience in electronic-structure theory or computational condensed matter physics, demonstrated by first-author publications

PREFERRED QUALIFICATIONS:
Candidates with experience developing or implementing computational methods, rather than only running standard DFT calculations, are especially encouraged to apply. Experience in one or more of the following areas is desirable:
  • Strongly correlated electron systems
  • Rare-earth and 4f-electron physics
  • Magnetism, magnetic anisotropy, and spin-orbit coupling
  • High-throughput computational workflows
  • Machine learning for materials discovery
  • Development or implementation of novel electronic-structure methods
  • Strong programming skills and experience with HPC environments
  • Strong written and oral communication skills

APPLICATION PROCEDURE:
Apply online at https://jobs.virginia.edu/us and search for R0083822. Attach the following documents: a cover letter, a curriculum vitae, and contact information for three references. Please note that multiple documents can be uploaded in the box.
APPLICATION DEADLINE:
Review of applications will begin on June 13, 2026, but the position will remain open until filled. The University will perform background checks on all new hires prior to employment.
Estimated salary range is $47,500 - $90,000, commensurate with experience.
This is a one-year appointment, and the appointment may be renewed contingent upon available funding and satisfactory performance.
For questions about the position, please contact Liqin Ke, Associate Professor, at uvn6ns@virginia.edu .
For questions regarding the application, please contact Rich Haverstrom, Academic Recruiter, at rkh6j@virginia.edu .
A competitive salary and benefits package is offered at University of Virginia. For more information on the benefits available to postdoctoral associates at UVA, visit postdoc.virginia.edu and hr.virginia.edu/benefits .
Job Profile
J0267 - Research Associate - 12 Month
Career Stream and Level
No Career Stream-No Level
The University of Virginia is an equal opportunity employer. All interested persons are encouraged to apply, including veterans and individuals with disabilities. Learn more about UVA's commitment to non-discrimination and equal opportunity employment .

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About University of Virginia

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The University of Virginia is distinctive among institutions of higher education. Founded by Thomas Jefferson in 1819, the University sustains the ideal of developing, through education, leaders who are well-prepared to shape the future of the nation.

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Colleges, universities, and professional schools

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Headquarters location

Charlottesville, VA, US

Year founded

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