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to incorporate these methods into models based on systematic testing and validation relative to current and historical data. This position will work closely with Virginia’s Land Use-Value Assessment Program
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for construction operations. The successful candidate will contribute to cutting-edge research in mixed reality (MR)-based simulation platforms, machine learning-based process optimization, and human-machine
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and optimization of measurement-based quantum computing protocols for quantum simulation of quantum many-body models. Preference will be given to candidates familiar with the stabilizer formalism and
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, cancer cells and stem-cell based methodologies. The successful candidate will join a collaborative and interdisciplinary project which integrates cancer biology, stem cells, organoids, microscopy
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, developing optogenetic and CRISPR-based logic circuits, and using Raman spectroscopy to monitor cellular responses and validate model predictions. The work will directly inform the design of multiplexed
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Qualifications • Ph.D. in fisheries, biology/ecology, statistics, or related fields. PhD must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility
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microenvironment. The project will employ genetically engineered mouse and human glioma models, along with advanced imaging techniques, single-cell and spatial transcriptomics, and molecular biology approaches
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responsible for preparing and utilizing chemical solutions in in-vitro experiments in a wet lab setting, maintain colonies of mouse models of human disease through husbandry and genotyping. The successful
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collaboration. Required Qualifications - Ph.D. in Forest Products, Forestry, Natural Resources, Sustainability, or a related field. PhD must be awarded no more than four years prior to the effective date
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behaviors. The selected candidate will be expected to help develop these additional projects. Required Qualifications - PhD in psychology or appropriate related discipline. PhD must be awarded no more than