Argonne
Postdoctoral Appointee – Materials Informatics and Autonomous Synthesis
Lemont, IL USA · Contract
Sponsorship not specified$73k-$121kDetected 29 days ago
PythonMachine LearningTensorFlowPyTorchscikit-learnPandasNumPyData ScienceNLPComputer VisionA/B TestingResearchExperimental DesignCommunicationCollaborationPublic Speaking
About the role
- This role is ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real laboratory workflows.
- Please note that the pay range information is a general guideline only.
- Argonne encourages everyone to apply for employment.
Responsibilities
- Build surrogate and predictive models that connect composition, molecular structure, synthesis and processing conditions, morphology, and device-relevant
- Contribute to strategies for generating diverse, high-value datasets, identifying meaningful descriptors and representations, and building reproducible computational pipelines, workflow automation, and data infrastructure that support long-term autonomous laboratory capabilities
Requirements
- Recent or soon-to-be-completed PhD (within the last 0-5 years) in chemistry, chemical engineering, materials science, polymer science, physics, computer science, and/or data science
- Excellent communication skills, the ability to work effectively in interdisciplinary teams
- Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork
Nice to have
- Experience with autonomous, self-driving, or robotic laboratory platforms
- Background in electronic polymers, conjugated polymers, organic semiconductors, soft materials, electrochemical materials, or related functional materials
- Experience with workflow automation, data infrastructure, database development, reproducible research pipelines, and collaborative environments that span computation, data science, and experiment
- Updated CV/Resume
- Unofficial Ph.D. transcripts
- If already awarded, a copy of the Ph.D. diploma
- Application Materials
- Postdoctoral Appointee
Compensation
- Please note that the pay range information is a general guideline only.
Benefits
- Develop machine learning-ready data resources for materials by integrating literature, in-house, and newly generated experimental data
- Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and improve data efficiency in autonomous platforms such as the Polybot
- Demonstrated accomplishments in materials informatics, scientific machine learning, or AI-guided experimental design
- Strong Python and scientific computing skills, including experience with tools such as NumPy, pandas, scikit-learn, and machine learning frameworks such as PyTorch, TensorFlow, or similar
- Experience developing surrogate models, predictive models, or adaptive learning workflows for scientific or engineering applications
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