Autonomous Infrastructure and Robotic Science Lead

The Computing, Environment, and Life Sciences (CELS) Directorate seeks an outstanding scientist to lead and support frontier research at the intersection of AI, autonomous platforms, data infrastructure, and domain science. The candidate will have established expertise across automated and autonomous experimental platforms and AI in addition to leadership of multi-disciplinary research programs and the development of novel research concepts. 

The scientist will lead Argonne’s Rapid Prototyping Laboratory (RPL), a team of computer scientists, roboticists, data scientists, and subject matter experts, who develop hardware and software infrastructure for laboratory autonomy, support autonomous laboratories in domains including chemistry, biology, and quantum science, work with domain scientists to execute autonomous experiments, and advance laboratory autonomy and robotics.

RPL develops the open-source Modular Autonomous Discovery for Science (MADSci) software framework for the orchestration of autonomous laboratories in addition to software infrastructure supporting the operation, training, and execution of robotic workflows. The scientist would be responsible for directing activities towards the advancement of these internal capabilities in addition to the support and development of collaborations across Argonne and beyond.

Focus Areas (expertise in one or more is highly desirable):

• 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

Key Responsibilities:

• Guide the development of infrastructure for laboratory autonomy including physical autonomous laboratories, robotics laboratories, and software frameworks for autonomous science and robotics
• Facilitate collaborations between the RPL and domain scientists across Argonne and partner institutions in the execution of successful autonomous science demonstrations
• Facilitate collaborations between the RPL and teams at partner institutions developing autonomous science and robotics infrastructure
• Guide the RPL team towards the advancement of laboratory autonomy and robotics
• Publish in refereed journals and present at conferences, symposia, and seminars
• Provide work direction and mentorship to postdoctoral appointees, research assistants, students, and technical staff
• Execute all activities in compliance with Argonne's safety policies, Safeguards and Security policies, work rules, and safe practices

Position Requirements

• Completed Ph.D. in Computer Science, Materials Science, Physics, Chemistry, or a related field, and a minimum of 4+ years of related 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, 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

Application Instructions:

Submit the following materials as attachments to your application:

• Cover letter detailing how your experience and expertise align with and will contribute to this position
• Curriculum vitae with publication list
• 1-page research statement outlining proposed research directions

About Argonne and the Rapid Prototyping Lab

Argonne National Laboratory is a U.S. Department of Energy multidisciplinary science and engineering research center, operated by UChicago Argonne, LLC. Argonne tackles the largest scientific and engineering challenges of our time, from clean energy and advanced materials to artificial intelligence and quantum information science.

The Rapid Prototyping Lab (RPL), in the Data Science and Learning division, develops integrated hardware and software solutions to accelerate scientific discovery through robotics and AI. RPL serves as a software and robotics hub where scientists collaborate, train the next-generation autonomous-discovery workforce, and develop open-source infrastructure for self-driving labs. RPL projects span new materials for energy storage, discovery of antimicrobial compounds, isotope production for medical applications, and more.

MADSci is RPL's flagship open-source software ecosystem and a core enabling technology for Argonne's broader Autonomous Discovery initiative, which aims to transform laboratory science by combining robotics, AI, and simulation to design, execute, and learn from experiments at unprecedented scale.

For more information:

• Rapid Prototyping Lab: https://rpl.cels.anl.gov/
• Autonomous Discovery at Argonne: https://www.anl.gov/autonomous-discovery
• MADSci on GitHub: https://github.com/AD-SDL/MADSci
• AD-SDL organization on GitHub: https://github.com/AD-SDL

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Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.