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/or observationally motivated model-building for SMBH feeding and feedback. The group integrates observations, theory, data analysis, algorithm development, model-building, and numerical simulations
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
development of data processing tools. The position is for one year with the possibility of reappointment for an additional year. It will involve developing, implementing, and validating novel algorithms
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on using unstructured and overset meshes with high-fidelity algorithms to obtain scale-resolved data. Candidate will also post-process data using data-driven and physics-driven methods to extract fundamental
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place technologies and to develop digital twin algorithms to assist clinicians in developing treatment plans. Analyzes complex sensor data, works with a multidisciplinary team to develop health digital
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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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Science and Engineering, or a related area is required. The position will involve developing models and algorithms for the evolution of inorganic aerosols in the atmosphere, building upon the research
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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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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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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