Assistant Scientist - AI for Autonomous Synthesis and Multimodal Characterization

About the role

Argonne National Laboratory is seeking an Assistant Scientist to advance the integration of artificial intelligence and machine learning in the synthesis of nanoscale and quantum materials. This position focuses on creating innovative workflows that combine synthesis with real-time characterization techniques. The ideal candidate will work at the intersection of nanofabrication and advanced measurement tools to facilitate autonomous exploration of complex material designs.

Key facts

What you'll do

• Spearhead a research initiative centered on AI-driven autonomous materials synthesis.
• Create and execute experimental workflows that merge synthesis, characterization, and decision-making processes.
• Develop and implement AI/ML techniques for optimization, active learning, and experimental planning.
• Construct analysis tools for high-throughput experimental data, focusing on real-time processing capabilities.
• Collaborate with scientists across various disciplines, including materials synthesis, characterization, and computational science.
• Contribute to the development of scalable workflows that span edge computing, beamline operations, and high-performance computing environments.
• Publish research findings in peer-reviewed journals and present at scientific conferences, while influencing future directions in autonomous materials research.

Requirements

• Ph.D. in physical chemistry, inorganic chemistry, computational materials science, chemical engineering, or a related discipline, with 3 to 6 years of postdoctoral experience.
• Strong knowledge of nanomaterials synthesis and in situ/operando x-ray characterization techniques, with demonstrated experience in both areas.
• Proven track record in developing and applying AI/ML methods for autonomous experimentation and optimization.
• A solid publication history showcasing innovation in AI/ML applications related to materials synthesis and synchrotron experiments.
• Familiarity with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
• Experience with optimization and active-learning libraries like BoTorch, GPyTorch, or scikit-learn.
• Proficient programming skills in Python, particularly in integrating with experimental control systems.
• Commitment to Argonne's core values: impact, safety, respect, integrity, and teamwork.

Nice to have

• Experience with experimental control frameworks such as ROS, Bluesky, or EPICS.
• Familiarity with laboratory automation and robotic synthesis technologies.
• Knowledge of generative models, reinforcement learning, or agentic AI for materials discovery.
• Skills in multimodal data fusion and real-time data reduction for synchrotron or nanoscale experiments.
• Understanding of high-performance computing workflows and scientific data infrastructure.
• Experience with digital twins, physics-informed machine learning, or simulation-enhanced experimental design.
• Strong communication abilities, both written and verbal, with a capacity to thrive in a collaborative, multidisciplinary setting.

Practical notes

The anticipated salary range for this role is between $94,486.00 and $147,398.94. The final compensation will be influenced by various factors, including the candidate's qualifications and relevant experience. Comprehensive benefits are included in the overall compensation package.

Application Materials

To apply, please submit the following documents:

• Curriculum Vitae (CV)
• Cover Letter

Argonne National Laboratory is an equal opportunity employer and is committed to fostering a diverse and inclusive workplace. All qualified applicants will be considered for employment without regard to any protected characteristic. Employment offers are contingent upon a background check and may require government access authorization.