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Field
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space through spatial ‘omics approaches. Responsibilities and qualifications The successful candidate should be eager to pursue ambitious research questions using latest-technology workflows and chart new
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partial reprogramming techniques to reverse age-related decline in health, focusing on improving healthspan and organ function in aging models. Key recent publications from Dr. Sahu’s previous work include
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to build predictive models Collaborate closely with experimentalists and modelling experts Project Environment This position is part of a collaborative research project involving: Two PhD students at TU
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of five scientific groups in the CFN, and supports the Electron Microscopy facility, which is the largest CFN scientific facility. The CFN is acquiring two new customized, ultra-high spatial resolution and
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transmission modeling, statistical modeling, spatial data analysis, and cost-effectiveness analysis. In parallel, we conduct research on vaccine-preventable infections, developing and evaluating predictive
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the following directions: 1) Developing computational methods for integrating multi-modal data, such as scRNA-seq, scATAC-seq, spatial transcriptomics, ChIP-seq, and CRISPR screening. 2) Investigating context
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, and spatial transcriptomics. Key responsibilities include: Developing AI/ML methods for image alignment across modalities Automated feature detection Predictive modeling of vascularization patterns
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of developmental biology using mass spectrometry imaging (MALDI-MSI). The candidate will focus on applying the insights of spatial molecular imaging using MALDI-MSI to define the molecular basis for development in
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large
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University of California, Los Angeles | Los Angeles, California | United States | about 2 months ago
models. We focus on creating organoids with precise cell types, architecture, and functions that closely mirror their in vivo counterparts. Our mission is to harness these advanced models to unravel