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independently and work in a collaborative, cross-disciplinary team. Desirable Expertise in user modelling, recommender systems, or other personalised technologies. Publications in top-tier conferences, such as
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advanced manufacturing; nanodevices and microsystems; physical science; and radiation effects and high energy physics, including all aspects of engineering diagnostics and testing, and modeling and
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of emerging science, and support the strategic dissemination of data that advances patient care worldwide. Through a phased training model, the program emphasizes both foundational knowledge and leadership
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(PRC). Works with the medial affairs team to evaluate and review promotional materials. Seeks to build and maintain collaborative relationships with all involved in the PRC process and sets a model for
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 4 hours ago
and oral communication A team science model Collaborative approach to research and training Initiative, independence, and integrity Ability to work on sensitive subject matter in a mature manner
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independently and work in a collaborative, cross-disciplinary team. Desirable Expertise in the application of foundation models and LLMs in healthcare/medical settings. Publications in top-tier conferences
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or simulation data to inform design decisions and validate performance models. Collaboration and Teamwork: Demonstrated experience working effectively in interdisciplinary research teams, with the ability
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microbiology, bacterial genetics, molecular biology, biochemistry, and murine models. Experience in bioinformatics and structural biology will be positively evaluated. Successful candidates will work with the
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for microbiome analysis. Publication record in peer-reviewed journals. Preferred Qualifications Experience with colorectal cancer models or inflammatory bowel disease (IBD). Familiarity with metabolomics (LC-MS
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measurement quality issues related to respondent non-compliance in ecological momentary assessment or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models