Assistant Scientist — Data Driven and Autonomous Materials Discovery (CNM)
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About this role
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: https://cnm.anl.gov
Focus Areas (expertise in one or more is highly desirable)
- 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
Key Responsibilities
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
Computational and HPC support
- Support end-users with HPC operations and maintenance issues, job optimization and scheduling, workflow understanding, and software installation
Collaboration and mentorship
- 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
Professional growth and operations
- Work toward promotion from Assistant Scientist to Scientist
- Manage vendor relationships as needed (hardware, cloud, managed support services)
Safety, security, and stewardship
- Execute all activities in compliance with Argonne’s ES&H policies, Safeguards and Security policies, work rules, and safe practices
Position Requirements
- 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
RD2: Bachelors and 5+ years of experience, Masters and 3+ years, or PhD and 0+ years, or equivalent
How to Apply
Submit the following materials via the Argonne National Laboratory careers portal:
- 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