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of innovative approaches for neural circuit interrogation while collaborating with interdisciplinary research teams and mentoring graduate and undergraduate students. This is an excellent opportunity for early
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of innovative approaches for neural circuit interrogation while collaborating with interdisciplinary research teams and mentoring graduate and undergraduate students. This is an excellent opportunity for early
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timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization? We are particularly interested in: LLM-driven
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chronic illness. CEQL fosters interdisciplinary collaboration, mentorship, and innovation to address complex health challenges through rigorous scientific inquiry, implementation science, and community
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. Queries should be sent to [email protected] . Applications received by October 15, 2026 will receive full consideration. Information about the Center can be found at: http://crres.indiana.edu The Annual Security
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://indiana.peopleadmin.com . Cover letters should be addressed to: Dr. Sonia Lee, Search Committee Chair, Center for Research on Race and Ethnicity in Society, Indiana University. Queries should be sent to [email protected]
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bioinformatics tools for integration of multi-omics and gene regulatory networks. The long-term research goal is to answer the key scientific question “How non-coding genetic variant act through context specific
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addressed to: Professors Erica Cartmill and Jacob Foster, Center for Possible Minds, Indiana University, 815 10th St., Bloomington, IN 47405. Queries should be sent to [email protected] and [email protected]
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with omics data analysis, biostatistics, and image analysis tools. Strong programming skills (R, Python) and knowledge of relevant databases and pipelines. Candidates with peer-reviewed publications
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timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization? We are particularly interested in: LLM-driven