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Field
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their skills to this system, together with experience in one or more of: optogenetics, functional imaging, molecular biology, immunohistochemistry, behavioural analysis or computational modelling. This is a
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applications to medical data. The project will combine theoretical work on multiparameter persistence with the design and implementation of algorithms for medical image analysis. The successful candidate will
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phenotyping, including Seahorse extracellular-flux analysis. Use flow cytometry and cell sorting to isolate and phenotype human epithelial and immune-cell populations. Conduct confocal imaging of mitochondrial
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molecular cloning, CRISPR, or genetic circuit design Experience with flow cytometry, confocal microscopy, or in vivo imaging (bioluminescence, fluorescence) Computational skills for data analysis, signal
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collaborative skills. Applicants from adjacent fields who wish to bring complementary expertise to the laboratory are also encouraged to apply. Experience in bioinformatics or in the analysis of genomic
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performing data analysis to create a robust and reliable testing protocol. Key Responsbilities: Conduct a comprehensive literature review on food rheology, texture analysis, particle analysis, image analysis
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delta-pair-distribution-function analysis will isolate correlated magnetic and atomic disorder to build a full picture of charge transport in these important materials. The role is a fixed term position
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 20 hours ago
, data acquisition, controls, or AI-enabled building systems. • Experimental design, laboratory testing, prototype development, and quantitative data analysis. • Research documentation, peer-reviewed
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and molecular characterization, including advanced imaging where appropriate. Integrate and interpret imaging, molecular and omics-derived datasets. Contribute to the analysis of clinical samples and
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include workload analysis, system prototyping, benchmarking, and experimental validation on local and national supercomputing platforms. The RA will collaborate with faculty and domain scientists to address