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The Role Applications are invited for a Postdoctoral Research Associate position in the Department of Engineering, with a focus on advanced solid oxide fuel cell (SOFC) modelling, three-dimensional
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, genomics, or computational analysis of high-dimensional datasets. Experience with one or more of the following: Flow cytometry Single-cell sequencing Spatial transcriptomics Multiplex imaging Bioinformatics
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Equation, Stochastic simulation algorithms, and approximation methods. ● Experience with single-cell or spatial transcriptomic data analysis. ● Familiarity with machine learning and deep learning
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New York University Grossman School of Medicine | New York City, New York | United States | 3 months ago
their downstream signaling pathways. These studies involve the identification and characterization of ICTs in T and B cells, investigation of the molecular mechanisms by which these ICTs control cell
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cells and adapting them for use with in implantable bio integrated device environments. The postdoctoral associate will design and execute studies of cell viability, growth, functionality, optical
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: Develop multiscale ABMs of multi-cellular immune cell populations and virus interactions. Design, implement, analyze, and validate agent-based, ODE, PDE, stochastic, hybrid, or surrogate models, selecting
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or other related fields; and be skilled in cell culture, microfluidic fabrication, modeling organ systems, characterization assays such as PCR, immunostaining, flow cytometry, ELISA etc. PREFERRED
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% Plan and conduct assigned and/or original research projects autonomously while working collaboratively with UM faculty, staff, postdocs and students. Perform basic molecular biology and cell biology
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in the analysis and interpretation of single-cell multi-omic data. This role will contribute to cutting-edge research by integrating statistical and computational approaches to address complex
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in the growth of melanoma cell xenografts in isogenic mice; to determine how GATA6 modulates the tumor immune microenvironment (immunohistochemical staining of tumor sections, transcriptomic analyses