Postdoctoral Scholar – AI for Functional Polymer Discovery at Lawrence Berkeley National Laboratory

Berkeley, California, United States -

Full Time

Start Date

Immediate

Expiry Date

27 Apr, 26

Salary

94020.0

Posted On

27 Jan, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Molecular Dynamics Simulation, Quantum Chemistry, Machine Learning, Generative Models, Dielectric Materials, Polymer Synthesis, Data Analysis, Interdisciplinary Collaboration, Research Communication, Active Learning, Uncertainty Quantification, Model Interpretability, High-Throughput Molecular Dynamics, HPC Experience, Software Engineering Practices, FAIR Data Practices

Industry

Research Services

Description

Lawrence Berkeley National Laboratory is hiring a Postdoctoral Scholar – AI for Functional Polymer Discovery within the Molecular Foundry division to support a newly funded two-year project focused on AI-driven discovery of high-performing polymer dielectrics for next-generation power electronics. In this role, the Postdoctoral Researcher will develop and implement a multimodal AI–digital twin framework that connects generative polymer design with physics-based multiscale simulations, machine-learning surrogate models, and experimental validation in a closed-loop workflow. The work will integrate machine learning, quantum chemistry, molecular dynamics, and experimental feedback, leveraging Berkeley Lab’s computational resources and the Foundry’s capabilities in polymer synthesis, processing, and dielectric characterization, within a highly interdisciplinary environment with opportunities for high-impact publications, open datasets, and prototype demonstrations. You will: Develop and implement generative molecular design workflows for polymer dielectrics using reaction-aware chemical rules, monomer libraries, and transformer-based chemical language models. Perform physics-based simulations (e.g., molecular dynamics, DFT) and implement Machine-Learned Interatomic Potentials (MLIPs) to predict physical parameters. Build and train machine-learning surrogate models (e.g., GNNs). Integrate simulation, ML, and experimental results into a closed-loop AI-digital twin framework utilizing uncertainty-guided active learning to improve predictive accuracy. Collaborate with experimental scientists to translate model predictions into synthesis and testing priorities. Analyze and interpret structure–property relationships using interpretable ML tools such as SHAP or attention maps. Communicate research progress through internal presentations, external conference talks, and peer-reviewed publications. Contribute to open datasets, codes, and best practices supporting reproducible AI-enabled materials discovery. We are looking for: Ph.D. (within the last two years) in Materials Science, Polymer Science, Chemistry, Chemical Engineering, Physics, or a related field. Strong background in at least one of the following areas: Molecular dynamics simulation and quantum chemistry calculation on polymers Machine learning applied to materials or chemistry (e.g., GNNs, Generative Models). Dielectric materials or functional polymers Demonstrated ability to conduct independent research and collaborate in interdisciplinary teams. Strong written and verbal communication skills, evidenced by peer-reviewed publications. Desired skills/knowledge: Experience with machine learning frameworks (e.g., PyTorch, TensorFlow) and transformer-based architectures; Familiarity with high-throughput molecular dynamics, DFT, or ML-based interatomic potentials (e.g., DeepMD, MACE); Experience working with polymer synthesis, processing, or dielectric characterization; Experience with active learning, uncertainty quantification (UQ), multimodal data fusion, model interpretability methods (e.g., SHAP); Experience working in collaborative environments. -Demonstrated research software engineering practices (clean code, Git, testing, packaging/workflow automation). Practical HPC experience (schedulers, scaling runs, workflow tools like Snakemake/Parsl/FireWorks—any similar evidence). Familiarity with FAIR-ish data practices (metadata standards, reproducibility, dataset governance). Experience collaborating with experimentalists and translating computational results into experimental decisions. Required Application Materials: CV Cover Letter (1 page) Publication List Additional information: Application date: Applications will be accepted until the job posting is removed. Appointment type: This is a full-time 2 year, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds and ongoing operational needs. You must have less than 3 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree. Salary range: This position is represented by a union for collective bargaining purposes. The salary range for this position is $84,336 - $94,020. Postdoctoral positions are paid on a step schedule per union contract and salaries will be predetermined based on postdoctoral step rates. Each step represents one full year of completed post-Ph.D. postdoctoral experience. Background check: This position is subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment. Work modality: Work will be primarily performed at: Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. A REAL ID or other acceptable form of identification is required to access Berkeley Lab sites (for more information click here). Want to learn more about working at Berkeley Lab? Please visit: careers.lbl.gov Equal Employment Opportunity Employer: The foundation of Berkeley Lab is our Stewardship Values: Team Science, Service, Trust, Innovation, and Respect; and we strive to build community with these shared values and commitments. Berkeley Lab is an Equal Opportunity Employer. We heartily welcome applications from all who could contribute to the Lab's mission of leading scientific discovery, excellence, and professionalism. In support of our rich global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories under State and Federal law. Berkeley Lab is a University of California employer. It is the policy of the University of California to undertake affirmative action and anti-discrimination efforts, consistent with its obligations as a Federal and State contractor. Misconduct Disclosure Requirement: As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct, are currently being investigated for misconduct, left a position during an investigation for alleged misconduct, or have filed an appeal with a previous employer.

Responsibilities

The Postdoctoral Researcher will develop and implement a multimodal AI-digital twin framework for polymer dielectrics, integrating machine learning and experimental validation. Responsibilities include conducting simulations, building machine-learning models, and collaborating with experimental scientists.