Berkeley Lab · 1 day ago
Postdoctoral Scholar – AI for Functional Polymer Discovery
Bay Area, California, United States
Full-time
Onsite
New Grad, Entry Level
$84K/yr - $94K/yrLawrence 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. The role involves developing and implementing a multimodal AI–digital twin framework that integrates machine learning, quantum chemistry, and experimental validation to improve predictive accuracy in polymer design and synthesis.
Research
Responsibilities
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
Qualification
Required
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
Preferred
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
Company
Berkeley Lab
Berkeley Lab is a national laboratory that creates advanced new tools for scientific discovery.
Founded in 1931
1001-5000 employees
H1B Sponsorship
Berkeley Lab has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
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Trends of Total Sponsorships
2025 (154)
2024 (159)
2023 (163)
2022 (154)
2021 (165)
2020 (107)
Funding
Current Stage
Late StageLeadership Team
Mary Barnum, MBA
Business Manager, COO Office
Rebecca Rishell
Deputy Chief Operating Officer
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