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design, develop, and evaluate AI-driven scientific visualization assistants that support intuitive, context-aware interaction with large-scale simulation and experimental data. The postdoc will focus
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, Information & Computing Sciences, Physics, Chemistry and Mathematics. Together, we work on excellent research and inspiring education. We do so, driven by curiosity and supported by outstanding infrastructure
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that can serve as references for future studies, (ii) enhancing the understanding of physical mechanisms and causal pathways to strengthen attribution analyses and model development, build operationalized
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optimization schemes. From developing AI models to uncover structure-function relationships with limited data sets, to building automated electrode-electrolyte interface discovery workflows and implementing full
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communities using model and non-model organisms. The individual is required to use tools in chemical ecology, genomics, molecular biology, and AI-driven predictive modeling in the research project. Specific
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models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis algorithms for critical equipment (e.g., compressors, reaction kettles). Research
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Strong written and oral communication skills Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred Qualifications Experience with block copolymer patterning
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, 41512056). This position will have the opportunity to gain strong training in cancer metabolism, cell growth signaling, and mouse models. The position is aiming at publishing high-profile papers and pursuing
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Postdoctoral Associate - Seed Biology and Technology Program The College of Agriculture and Life Sciences (CALS) is a pioneer of purpose-driven science and Cornell University’s second largest
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Postdoctoral Associate - EEB - Angrawal Lab The College of Agriculture and Life Sciences (CALS) is a pioneer of purpose-driven science and Cornell University’s second largest college. We work across