Responsibilities
- Design and test self-directed experimental workflows in fields like materials science, chemistry, advanced manufacturing, or biology, converting lab-scale methods into repeatable automated processes.
- Combine laboratory instruments, robotic systems, sensors, and software into unified platforms capable of autonomous or semi-autonomous operation that is safe, consistent, and scalable.
- Build software and data systems to support scientific research, including instrument control, data collection, experiment coordination, workflow automation, metadata recording, and traceability.
- Apply artificial intelligence and machine learning techniques to scientific experimentation, including planning, adaptive testing, predictive modeling, optimization, and data analysis.
- Support interdisciplinary research by diagnosing system issues, documenting outcomes, publishing findings, presenting at conferences, and working with experts in science, engineering, robotics, and AI.
- Communicate research results through presentations at seminars and technical meetings, assist in shaping research direction, and collaborate within diverse scientific teams.
- Carry out additional assigned tasks in support of research objectives.
Compensation
Based on experience and qualifications
Work Arrangement
Onsite
Team
Multidisciplinary research team focused on automation, robotics, and AI in scientific experimentation
Responsibilities
- Design and validate autonomous experimental workflows for materials science, chemistry, advanced manufacturing, biology, or related scientific domains, transforming research questions and bench-scale procedures into reproducible automation protocols.
- Integrate instruments, robotics, sensors, and software systems into coordinated autonomous or semi-autonomous laboratory platforms that enable safe, reliable, and scalable experimentation.
- Develop software and data infrastructure in support of research campaigns for instrument control, data acquisition, experiment orchestration, workflow automation, metadata capture, traceability, and integration with laboratory information or data management systems.
- Apply AI/ML methods to experimental discovery, including experiment planning, adaptive experimentation, predictive modeling, optimization, and analysis of experimental and instrument data.
- Contribute to multidisciplinary research campaigns by troubleshooting platform performance, documenting and communicating results, publishing in peer-reviewed journals, presenting at conferences, and collaborating with scientists, engineers, roboticists, automation engineers, and AI/ML researchers.
- Present research findings at seminars, conferences, and technical meetings; contribute to research design and project execution; collaborate in a multidisciplinary team environment; and publish results in peer-reviewed journals.
- Perform other duties as assigned.
Not available