Assistant Scientist – AI for Autonomous Synthesis and Multimodal Characterization

What you'd do

• Lead and develop a research program in AI-enabled autonomous materials synthesis
• Design and implement closed-loop experimental workflows that integrate synthesis, characterization, and decision-making
• Develop and apply AI/ML methods for active learning, optimization, inverse design, and experiment planning
• Build analysis tools for multimodal, high-throughput experimental data, including real-time or near-real-time processing
• Collaborate closely with scientists across materials synthesis, characterization, beamline science, theory, and computing
• Contribute to the development of scalable computational and data workflows spanning edge, beamline, and HPC environments
• Publish in peer-reviewed journals, present at scientific meetings, and help shape future directions in autonomous materials research

What they want

• Ph.D. in physical chemistry, inorganic chemistry, computational materials science, chemical engineering, or a related field, along with 3–6 years of postdoctoral research experience
• A strong understanding of nanomaterials synthesis and/or in situ/operando x-ray characterization (including scattering, spectroscopy, or imaging), with demonstrated experience connecting the two
• Proven experience developing and applying AI/ML methods to autonomous experimentation, closed-loop optimization, active learning, or inverse design
• A strong publication record demonstrating innovation in AI/ML for materials synthesis, synchrotron experiments, or a closely related area
• Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX
• Experience with optimization and active-learning libraries such as BoTorch, GPyTorch, or scikit-learn
• Strong programming skills, especially in Python, including integration with experimental control systems or lab-automation frameworks
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork