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design. The successful candidates will contribute significantly to the group’s mission in leveraging AI for scientific discovery. The successful candidates will conduct groundbreaking research
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-learning methods, knowledge-graph and ontology-based scientific data infrastructures, and agentic workflows for autonomous hypothesis generation, mechanistic exploration, and design of catalytic systems
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coordination, and fostering a collaborative research environment. The position is designed to support the candidate’s professional development through opportunities in scientific publishing, grant writing
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with experts in AI and machine learning. The post-doctoral associate will also be responsible for data management and dissemination of scientific results. The successful candidate will join a
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applying these tools to catalytic reaction engineering and methane conversion chemistry. Required Qualifications - Ph.D. in Chemical Engineering, Chemistry, Materials Science, Physics, or a closely related
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a professional profile positioned for success in academia, biotech, or pharmaceutical science. Why work in the Gregus/Buczynski Lab? work in the Gregus/Buczynski Lab? • Strong funding & project
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conducts independent and collaborative research activities focused on advancing scientific knowledge and improving clinical outcomes. The successful candidate will contribute to the design, implementation
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to the effective date of appointment with a minimum of one year eligibility remaining. • Experience in computational data analysis and scientific programming (e.g., Python, MATLAB, or similar). • Demonstrated
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and collaborative researcher with expertise in engineering, materials science, or related computational fields to contribute to our research program focused on transport packaging optimization and
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- Publication record that shows the ability to communicate scientific contributions through the literature - Evidence of oral scientific communication skills (i.e., presentations at conferences). Overtime Status