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

This is an exciting opportunity to help shape a new generation of closed-loop, AI-enabled experimental workflows that tightly integrate synthesis within situ and operando x-ray, electron, and optical characterization. The successful candidate will help bridge CNM’s world-class capabilities in nanofabrication and chemical synthesis with APS’s leading synchrotron measurement tools, enabling adaptive and autonomous exploration of complex materials design spaces.

In this role, you will lead a research program centered on AI-driven autonomous synthesis, including:

• Active learning and Bayesian optimization over synthesis parameters such as precursors, temperature, sequences, and pressure

• Generative and inverse-design models for materials discovery

• Closed-loop feedback frameworks that use in situ/operando scattering, spectroscopy, and imaging to guide synthesis in real time

• AI-enabled analysis of high-throughput, multimodal experimental data with uncertainty quantification

• Integration of edge computing, high-performance computing (HPC), and scientific data infrastructure to support scalable, user-facing autonomous workflows across CNM synthesis platforms and APS beamlines

This position is a joint appointment between the Theory and Modeling Group at CNM and the Computational Science and AI Group (CAI) at APS. The successful candidate will have access to Argonne’s exceptional ecosystem of facilities and expertise, including the upgraded APS, CNM’s advanced synthesis and characterization capabilities, and leadership-class computing resources at the Argonne Leadership Computing Facility.

Key Responsibilities

• 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

Position Requirements

• 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

Preferred Qualifications

• Experimental control and orchestration frameworks such as ROS, Bluesky, or EPICS

• Laboratory automation and robotic synthesis platforms

• Generative models, reinforcement learning, or agentic AI approaches for materials discovery and experiment planning

• Multimodal data fusion and real-time data reduction for synchrotron or nanoscale experiments

• High-performance computing (HPC), edge-to-HPC workflows, and scientific data infrastructure

• Digital twins, physics-informed machine learning, or simulation-augmented experiment design

• Excellent written and verbal communication skills, with the ability to work effectively in a highly collaborative, multidisciplinary environment

Application Materials

Please upload the following as part of your application:

• Curriculum Vitae (CV)

• Cover Letter

RD2: Bachelors and 5+ years of experience, Masters and 3+ years, or PhD and 0+ years, or equivalent

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Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.