AI-Driven Autonomous Materials Synthesis Scientist

argonne; Lemont (IL); On-site; USD 94,486 - 147,399; Full time

Job summary

Argonne National Laboratory’s Center for Nanoscale Materials (CNM) and Advanced Photon Source (APS) invite applications for a joint Assistant Scientist position focused on developing and applying AI/ML methods for autonomous, self-driving synthesis of nanoscale and quantum materials.

This joint appointment bridges CNM’s synthesis capabilities with APS beamlines, enabling AI-driven closed-loop experimental workflows integrated with in situ/operando characterization.

Qualifications

• Ph.D. in physical chemistry, inorganic chemistry, computational materials science, chemical engineering, or a related field with 3–6 years postdoc experience
• Proven experience applying AI/ML methods to autonomous experimentation, closed-loop optimization, active learning, or inverse design
• Strong publication record in AI/ML for materials synthesis, synchrotron experiments, or related areas
• Experience with deep learning frameworks (PyTorch, TensorFlow, or JAX)
• Experience with optimization/active-learning libraries (BoTorch, GPyTorch, scikit-learn)
• Strong programming skills in Python and integration with experimental control systems
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork

Responsibilities

• Lead a research program in AI-enabled autonomous materials synthesis
• Design and implement closed-loop experimental workflows integrating synthesis, characterization, and decision-making
• Develop AI/ML methods for active learning, optimization, inverse design, and experiment planning
• Build tools for multimodal, high-throughput data with real-time processing
• Collaborate with researchers across materials synthesis, characterization, beamline science, theory, and computing
• Contribute to scalable computational and data workflows across edge, beamline, and HPC environments
• Publish in peer-reviewed journals and present at meetings

Skills

AI/ML methods

Active learning

Bayesian optimization

Experiment planning

Multimodal data analysis

Python programming

Computation workflows

Collaborative research

Education

Ph.D. in physical chemistry, inorganic chemistry, computational materials science, chemical engineering, or related field

Tools

PyTorch

TensorFlow

JAX

BoTorch

GPyTorch

scikit-learn

Python

Job description

Argonne National Laboratory’s Center for Nanoscale Materials (CNM) and Advanced Photon Source (APS) invite applications for a joint Assistant Scientist position focused on developing and applying AI/ML methods for autonomous, self-driving synthesis of nanoscale and quantum materials.

This joint appointment bridges CNM’s synthesis capabilities with APS beamlines, enabling AI-driven closed-loop experimental workflows integrated with in situ/operando characterization.