Autonomous Materials Synthesis AI Scientist

Argonne National Laboratory; Lemont (IL); On-site; USD 110,000 - 160,000; Full time

Job summary

Argonne National Laboratory's CNM and APS invite applications for a joint Assistant Scientist position focused on AI/ML for autonomous nanoscale materials synthesis. We aim to enable closed‑loop, AI‑enabled experimental workflows that tightly integrate synthesis with in situ and operando characterization such as X-ray, electron, and optical measurements.

The role will bridge CNM’s nanofabrication and chemical synthesis with APS’s synchrotron tools to enable adaptive materials exploration,

Qualifications

• PhD in a related field with 3–6 years of postdoctoral experience.
• Strong background in AI/ML for autonomous experimentation and closed-loop optimization.
• Proven publication record in AI/ML for materials synthesis or synchrotron experiments.
• Experience with PyTorch, TensorFlow, or JAX.
• Programming in Python with experimental control or lab automation integration.

Responsibilities

• Lead a research program in AI-enabled autonomous materials synthesis.
• Design closed-loop workflows integrating synthesis, characterization, and decision‑making.
• Develop AI/ML methods for active learning and inverse design.
• Create analysis tools for multimodal, high-throughput data with real‑time processing.
• Collaborate across materials synthesis, beamline science, theory, and computing.

Skills

AI/ML methods

Python programming

Active learning

Bayesian optimization

Experiment planning

In situ/operando characterization

Multimodal data analysis

Education

Ph.D. in physical chemistry / inorganic chemistry / computational materials science / chemical engineering

Tools

PyTorch

TensorFlow

JAX

BoTorch

GPyTorch

scikit-learn

ROS

EPICS

Bluesky

Job description

Argonne National Laboratory's CNM and APS invite applications for a joint Assistant Scientist position focused on AI/ML for autonomous nanoscale materials synthesis. We aim to enable closed‑loop, AI‑enabled experimental workflows that tightly integrate synthesis with in situ and operando characterization such as X-ray, electron, and optical measurements.

The role will bridge CNM’s nanofabrication and chemical synthesis with APS’s synchrotron tools to enable adaptive materials exploration,