Autonomous Infrastructure and Robotic Science Lead at Argonne · Lemont

Autonomous Infrastructure and Robotic Science Lead
Location: Lemont, IL USA
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:
Evaluate the performance of assigned staff; recommends professional development, salary actions, and promotions
Play a key role in the recruitment and selection of high-quality staff
Report on research progress and new initiatives to division management, review committees, and funding agencies
Provide supervisory oversight including developing, motivating, and leading a team of professionals
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 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, supervisory oversight, and mentorship, including development and motivation, to postdoctoral appointees, research assistants, students, and professional technical staff
Execute all activities in compliance with Argonne’s ES&H policies, Safeguards and Security policies, work rules, and safe practices
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
Position Requirements
Minimum of a Ph.D. in Computer Science, Materials Science, Physics, Chemistry, or a related field and 4+ years of experience, or equivalent
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
Preferred experience leading and/or managing others from students to professional staff
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 and contact information for three professional references
1-page research statement outlining proposed research directions
Job Family
Research Development (RD)
Job Profile
Computational Science Leader 1
Worker Type
Regular
Time Type
Full time
The expected hiring range for this position is $148,125.00 - $231,075.00.
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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