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.
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Job Type
Full-time
Career Level
Mid Level
Education Level
Ph.D. or professional degree
Number of Employees
1,001-5,000 employees