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the application of machine learning techniques (e.g., doc2vec, encoder models, multi-modal embeddings, large language models) to map concepts and their relationships, tracing how they change, merge, or diverge
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Employee Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job The primary responsibility of this postdoc position will be to probe for Beyond the Standard Model physics in the Mu2e
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data modeling skills. Use of reproducible research methods and software version control (e.g. git/GitHub). Experience using high performance computing (HPC) environments and job scheduling. Pay and
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model development. -Proficiency in molecular and cellular biology techniques (e.g., DNA/RNA extraction, flow cytometry, histology, fluorescence imaging). -Proven record of first-author publications in
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aging using genetically modified mouse models. The laboratory has demonstrated that immune aging drives systemic aging including senescence in multiple tissues, liver and kidney dysfunction, disc
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machine learning analyses will be performed to determine correlations across stimulation settings and body systems as well as to develop predictive models and biomarkers for physiological and clinical
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Qualifications: Strong background in modeling and analytical skills using OpenSees and Abaqus. Experience in large-scale testing. Previous design experience in North America is considered an asset. Proficiency in
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Responsibilities: Conduct basic, translational, and clinical research involving animal models and/or human subjects. Design and implement experiments related to auditory perception and neural coding Develop and
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learning models for robust crop row detection. Implement post-processing techniques on segmentation masks to enhance lane-finding accuracy. Develop and implement sensor fusion and SLAM (Simultaneous
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across disciplines, 2) collaborative projects that assemble the people required to achieve that integration, and 3) strategic use of model systems that provide the most powerful experimental access