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The researcher will work at the intersection of materials science, chemistry, data science, and laboratory automation to identify promising materials, guide experiments, and establish relationships
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of LLMs to accelerators, specifically, use of agentic AI to support intelligent system analysis, aid operator decision-making, automate complex workflows, and enable more adaptive approaches to machine
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, and building reproducible computational pipelines, workflow automation, and data infrastructure that support long-term autonomous laboratory capabilities Share research outcomes through publications
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. We seek a highly motivated candidate to contribute to an interdisciplinary research effort at the intersection of materials science, laboratory automation, and artificial intelligence. This position
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, model evaluation, workflow automation, and AI-assisted orchestration of distributed learning tasks. The position will also provide opportunities to contribute to open-source software, publish research
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This position will be dedicated to research projects aiming developing and implementing experimental focused application of AI and automation tools for unraveling fundamental interfacial processes
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at the University of Chicago, automating screening protocols in partnership with Argonne National Laboratory Drive research at the intersection of automation, robotics, generative AI, and computational simulations
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scientific programming languages is a plus. Excellent oral and written communication skills. Experience with automation, control, and instrument infrastructure. Ability to model Argonne’s core values of impact
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solidified artificial intelligence (AI) as a key science accelerator, especially in concert with automated techniques for sample creation, movement, and evaluation. However, the flexibility and intelligence
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extraction and analysis of sequencing-based omics data. Experience in bioreactor configuration, inline/online sensor integration, and PID-based process control; familiarity with bioreactor automation systems