Autonomous Infrastructure and Robotic Science Lead
$148,125–$231,075 year
On-siteLemont, Illinois, United States
Job Summary
Lead and support frontier research at the intersection of AI, autonomous platforms, and domain science by directing activities toward the advancement of laboratory autonomy and robotics. Evaluate assigned staff for professional development, salary actions, and promotions while guiding the development of infrastructure for physical autonomous laboratories and software frameworks. Play a key role in recruiting high-quality staff, report on research progress to division management and funding agencies, and facilitate collaborations with partner institutions. Publish in refereed journals and present at conferences to advance the Rapid Prototyping Lab's open-source MADSci software framework and autonomous discovery initiatives.
Required Qualifications
- Minimum of a Ph.D. in Computer Science, Materials Science, Physics, Chemistry, or a related field
- 4+ years of experience
- Proven research track record in deploying automated and autonomous platforms and AI/ML towards accelerating science
- Demonstrated ability to formulate scientific problems relevant to the DOE portfolio
- Strong oral and written communication skills
- 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
- Ability to obtain a government access authorization
- Ability to pass a background check that includes an assessment of criminal conviction history
Desired Qualifications
- Preferred experience leading and/or managing others from students to professional staff
- Focus Areas (expertise in one or more): Autonomous laboratories for chemistry, materials, biology, etc.
- AI/ML for predictive modeling and inverse design
- 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
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