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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
. Familiarity with enzyme electrode fabrication, surface modification, and analytical assay development. Strong experimental design, data analysis, communication, and scientific writing skills are required
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern AI systems Requirements: PhD in Mathematics, Computer Science
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monitoring, control and predictive analysis during experimental operation. Both positions include reporting and publication of the results, and the opportunity to contribute to the supervision of PhD and MSc
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, experimental troubleshooting, scientific communication, and translation of complex technical concepts into practical experimental procedures. Opportunities will also be available to present research and
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. The questions we probe and the answers we seek focus on three overlapping concerns: We believe that achieving next-generation scientific breakthroughs requires an understanding of the world’s complex
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, intelligent experiment control, and scientific workflows. Join an interdisciplinary team at the interface of AI, microbiology, and laboratory automation to shape the future of data-driven live-cell analysis
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Qualifications PhD in epidemiology, public health, neuroscience, population health, or related field. Demonstrated experience interpreting complex longitudinal or trial-embedded datasets in collaboration with
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and algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern
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master levels and advancing research about the integration of cutting-edge AI and machine learning as tools to enhance the analysis, interpretation, and scalability of Remote Sensing data. The successful
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research profile focuses on development of novel statistical methods and software for the analysis of large and complex data sets. Applications include e.g., evaluation of large-scale achievement tests, and