Key ResponsibilitiesDesign, implement, and validate physics-informed AI/ML models for microelectronics materialsCurate, manage, and integrate heterogeneous datasets from experiments and simulationsCollaborate closely with experimental teams to benchmark and refine computational modelsDisseminate research through publications, presentations, and open-source contributionPosition RequirementsRecent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Data Science, Chemistry, Chemical Engineering, Electrical Engineering, Computer Science, Physics, or a related fieldDemonstrated proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow) applied to scientific problemsStrong background in managing multimodal datasetsProven experience collaborating with experimental teams to validate computational modelsAbility to model Argonne's core values of impact, safety, respect, integrity, and teamworkPreferred QualificationsDeep understanding of AI/ML concepts, including transformers, latent-space representations, generative models, and reinforcement learningExperience with high-performance computing, physics-based simulations, and multimodal data workflowsDemonstrated ability to train and deploy AI/ML models using simulated and experimental dataFamiliarity with agentic LLM-based approaches and related technologies (e.g., RAG, MCP, A2A)Interest in interfacial phenomena and defect dynamics in materials across scalesJob FamilyPostdoctoralJob ProfilePostdoctoral AppointeeWorker TypeLong-Term (Fixed Term)Time TypeFull timeThe expected hiring range for this position is $70,758.00-$117,925.00.Please note that the pay range information is a general guideline only. Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A.