Assistant Scientist - Data Driven and Autonomous Materials Discovery; CNM

The Theory and Modeling Group at the Center for Nanoscale Materials (CNM) seeks an outstanding Assistant Scientist to lead and support frontier research at the intersection of AI/ML, data infrastructure, autonomous systems, and materials science. You will develop and apply advanced data-driven methodologies to accelerate discovery in materials design, characterization, and synthesis. The role combines self-directed and collaborative research aligned with CNM strategic themes, alongside scientific support for CNM users.

CNM is a DOE Office of Science user facility that provides researchers worldwide with world-class expertise and instrumentation for multidisciplinary nanoscience and nanotechnology. Learn more about CNM themes at: (Use the "Apply for this Job" box below)..gov

• AI/ML for predictive modeling and inverse design of nanomaterials
• Autonomous laboratories for materials synthesis and characterization
• Generative models, reinforcement learning, and agent-based approaches to streamline experimentation and accelerate discovery
• Integration of HPC, data infrastructure, and ML pipelines for data-driven and autonomous research
• Digital twins and simulation-augmented AI tools
• Interfacing AI tools with experimental facilities at CNM and Argonne

Research leadership (50%)

• Develop and lead an independent and collaborative research program in computational materials science aligned with CNM strategic themes and the DOE mission
• Publish in refereed journals and present at conferences, symposia, and seminars
• Contribute to proposal development and assist with execution and reporting for CNM, DOE, and other sponsors

User program engagement (50%)

• Establish and maintain a vibrant, productive collaboration program with CNM users
• Provide scientific and technical support for user computational projects to ensure successful execution and growth of user-led research

• Support end-users with HPC operations and maintenance issues, job optimization and scheduling, workflow understanding, and software installation

• Collaborate with internal and external researchers to drive innovation in nanoscience and nanotechnology
• Contribute to CNM’s strategic scientific directions through pioneering R&D
• Provide work direction and mentorship to postdoctoral appointees, research assistants, students, and technical staff

• Work toward promotion from Assistant Scientist to Scientist
• Manage vendor relationships as needed (hardware, cloud, managed support services)

• Execute all activities in compliance with Argonne’s ES&H policies, Safeguards and Security policies, work rules, and safe practices

• Ph.D. in Materials Science, Physics, Chemistry, Chemical Engineering, Electrical Engineering, or a related field
• Proven research track record in computational materials science and AI/ML, with applications in areas such as quantum information science, energy capture/storage/conversion, or microelectronics
• Demonstrated ability to formulate scientific problems in the design and theory of nanoscale systems relevant to the DOE portfolio
• Considerable skill in data management and high-performance computing, including workflow design and optimization
• Strong oral and written communication skills, with the ability to work effectively with internal and external collaborators to achieve established goals
• Demonstrated ability to collaborate in a multidisciplinary environment and provide scientific guidance to a diverse research community
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork

• Cover letter detailing how your experience and expertise align with and will contribute to this position
• Curriculum vitae with publication list and contact information for three professional references
• 2-page research statement outlining proposed research directions
• 1-page statement describing your approach to engaging and growing the CNM scientific user program

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

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