Staff Scientist - AI​/ML and Autonomous Discovery Nanomaterials; CNM

Overview

The Center for Nanoscale Materials (CNM) at Argonne National Laboratory is seeking an outstanding staff scientist to lead and support cutting-edge research at the intersection of AI/ML, data infrastructure, autonomous lab systems, and nanomaterials science. This position emphasizes developing and applying advanced data-driven methodologies to accelerate discovery across materials design, experimental characterization, and synthesis, with an expectation of strong engagement with CNM users and close integration with CNM experimental facilities.

The successful candidate will be responsible in equal measure for self-directed, collaborative research aligned with CNM’s strategic plan and for user support, including enabling workflows that couple computation, AI, and experimental measurement.

Expertise in one or more of the following areas is highly desirable:

• AI/ML for predictive modeling and inverse design of nanomaterials and nanoscale systems
• AI-enabled analysis of experimental data (e.g., microscopy, spectroscopy, scattering, imaging) including uncertainty quantification, interpretability, and reproducibility
• Autonomous and semi-autonomous experimentation for materials synthesis and characterization (closed-loop optimization, active learning, adaptive measurement)
• Generative, reinforcement learning, and agentic approaches to streamline experimentation and accelerate discovery
• Integration of HPC, data infrastructure, and ML pipelines for scalable, user-facing data-driven research
• Digital twins and simulation-augmented AI tools for interpreting and planning experiments
• Interfacing AI and modeling tools with experimental platforms at CNM/Argonne (including in situ/operando workflows and facility-aware constraints)

The CNM is a DOE Office of Science user facility that provides researchers from across the globe with world-class expertise and instrumentation for multidisciplinary nanoscience and nanotechnology research. Beyond staff-led science, the prospective staff member will be expected to provide scientific and technical support to internal and external researchers, enabling impactful experiments and analysis, advancing AI-ready data practices, and strengthening experimental–computational integration across the user program.

• Develop and lead an independent and collaborative research program aligned with CNM strategic themes and the DOE mission (~50%). Publish results of independent and collaborative research in refereed journals and present at meetings, conferences, symposia, and seminars. Assist in the proper execution and reporting of projects for CNM, DOE, and other sponsoring sources
• Establish and maintain a vibrant and productive collaborative program with CNM users (~50%). Provide scientific and technical support for user projects spanning modeling, autonomous synthesis, AI/ML, and experimental data interpretation, ensuring successful execution and dissemination of user-led research
• Enable experimental–computational workflows by collaborating closely with experimental staff and users to integrate AI/ML and modeling with CNM characterization and synthesis capabilities (e.g., experiment planning, data reduction, automated analysis, multimodal data fusion, and in situ/operando interpretation)
• Stay abreast of progress in relevant fields and contribute to CNM and the broader user community through method development, best practices, and mentoring
• Support end-users with high performance computing and workflow needs (job optimization, workflow integration, data movement/management, software support) and with scalable analysis pipelines for experimental and simulation data
• Collaborate with internal and external researchers to drive innovation and discoveries in nanoscience and nanotechnology
• Contribute to the advancement of CNM strategic scientific directions through pioneering research and development
• Provide work direction to postdoctoral appointees, research assistants, students, and technical staff working on projects within the candidate’s area of responsibility
• Candidate may be required to manage vendor relationships as it pertains to hardware, cloud, and managed support…