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genetic knockouts in yeast and mammalian cell lines, and protein purification. Job Responsibilities: 35%: Computational algorithm development and data analysis 35%: Design and conduct experiments with yeast
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available single-cell sequencing data generated from patient samples and mouse models, we will enhance and apply machine-learning based algorithms to deconvolute bulk tumor RNA-seq samples to distinct immune
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data management sufficient to create, transform and integrate data in a variety of resolutions and formats. Analysis will include running machine learning algorithms (e.g., Random Forest, CART) and
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model algorithms for livestock enteric fermentation and manure housing and management using a scientific programming language. Integrate the modules into the APEX ecosystem simulation model. Assess and
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Requisition Id 15253 Overview: We are seeking a Postdoctoral Research Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted
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for transmission or distribution grids, synchronous generators, large loads, transmission networks, etc. Develop simulation algorithms that enable large-scale simulations. Integrate (or co-simulate) grid component
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reconstruction algorithms that incorporate multiply-beam coherent scattering imaging in a grazing incidence geometry to improve the spatial resolution to ultimately demonstrate the utility of the novel coherent
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magnetometry measurement and analysis of the Nab spectrometer magnetic fields, help develop the BL3 DAQ and algorithms, carry out Monte Carlo simulations for Nab and BL3, and help undergraduates at EKU finish
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surveys, data-intensive algorithms, and software. The Institute comprises seven faculty and senior fellows, and over 30 postdoctoral researchers, research scientists, software engineers, and graduate
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or accelerated acquisition and reconstruction algorithms will be highly valued. Instructions Interested candidates should apply via Interfolio link with their CV (including a full list of publications), a