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, and/or RStudio for high-dimensional flow cytometry data analysis and visualization. Experience with high-parameter spectral flow cytometry and/or mass cytometry (CyTOF). Experience with single-cell RNA
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experience in experimental design and quantitative characterization of microfluidic systems, including flow behaviour, plasma separation efficiency, and assay compatibility. • Hands-on expertise in the
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researcher with a strong background in atomization and experimental fluid mechanics, particularly in optical flow-visualization techniques. The successful candidate will conduct advanced laboratory
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solver, capable of running simulations on the Aurora supercomputer, using AMReX ( https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry
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, recharge, and subsurface flow in intensively managed fields. This position emphasizes the use of noninvasive geophysical methods to characterize spatial and temporal variability in soil moisture
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graduate training or previous position. Preferred Experience Candidates with experience in flow cytometry, single-cell analysis, quantitative imaging, and routine use of R or Python for data analysis
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/Mathematics/Physics. Expertise in either scientific programming (C/C++/Python/Matlab/R) or experimental immunology (Flow cytometry, next generation sequencing) with experience in scientific data visualization
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flow and contaminant transport, rather than relying solely on sequential or one-way model execution. The candidate will maintain water and contaminant mass balance across model interfaces; develop
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use of single-cell genomic techniques, high-throughput profiling, flow cytometry, molecular biology, and computational analysis on patient-derived samples and cell line models of hematologic
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, groundwater levels, surface-water flow, drainage discharge, precipitation, or water-quality parameters Experience with geographic information systems, relational databases, automated data workflows, or