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Field
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students, and technical staff to ensure alignment across objectives and timely progress on deliverables. In addition to research activities, the postdoctoral fellow will contribute to extension and
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transformative unclassified research that aligns with the strategic interests of the Intelligence Community (IC). To ensure the research agenda remains responsive to the pressing needs of effective intelligence
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PhD’s degree in mechanical engineering or related engineering field. Know multiple-scale and multi-phase numerical modeling and familiar with lattice Boltzmann simulations Having rich skills in coding in
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feasibility Alignment with QRI research thrusts and infrastructure Interdisciplinary potential and collaboration across departments Contribution to Michigan's expanding quantum ecosystem Program Requirements
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pipeline, ensuring real-time performance, scalability, and deployment readiness in alignment with the project’s research objectives. The successful candidate will work with a diverse team of faculty members
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employment patterns and labour market integration in BC Develop tables, figures and data narratives aligned with reporting requirements (Data abstraction and Visualization Skills (i.e. Histograms, tables
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developed for a range of important brain disorders, including neurodegenerative dementias, movement disorders and neuroinflammatory diseases such as multiple sclerosis, epilepsy and neuropsychiatric
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completed recruitment, and consists of whole genome sequences, comprehensive behavioral, phenotypic, and multi-omic data, and electronic health record linkage. Additionally, proteomic profiles of all 100,000
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security models and proofs, working prototypes, and clear evaluation methods that can translate into auditable, standards-aligned controls for high-assurance identity use cases. You will work with a cross
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structural biology to tackle challenging scientific questions. Your responsibilities will include, but are not limited to: Multi-omics analysis of bulk and single-cell sequencing data. Developing deep learning