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related field. You bring a strong analytical background and experience with optimization models and quantitative analysis methods. Familiarity with urban energy systems, energy infrastructures, or spatial
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transcriptional regulation from multi-omics data; computational method development for single-cell epigenomic sequencing and image-based spatial-omics data analysis; computational and experimental studies
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spatial accuracy of approximately 5 nm and temporal accuracy of 2 to 5 ms in cell cultures on coverslips. The aim of this project is to achieve the same performance in depth in biological tissues
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experience: behavioral and/or molecular neuroscience; or bioinformatics; Skills: brain surgery and behavioral tests on rodents, or advanced image and data analysis; Experience in iDISCO brain clearing, light
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(preferably in R and/or Python). b) Preferential factors: Familiarity with spatial data analysis and/or Machine Learning methods will be valued. Workplan and objectives to be achieved: The work plan aims
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) (Dr. Simpson’s webpage). The original call for the solicitation can be found here: https://www.energy.ca.gov/solicitations/2025-02/gfo-24-307-advancing-designs-and-analysis-high-voltage-direct-current
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to high-dimensional omics datasets. Familiarity with transcriptomic analysis tools (e.g., Seurat, Scanpy, DESeq2). Experience with spatial transcriptomics and multi-modal data integration is highly
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responsibility of developing predictive tools based on machine learning for the analysis and interpretation of Raman vibrational spectra applied to battery materials. The successful candidate will design and
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property that can be exploited to reduce the modal analysis to a single unit cell, i.e. the elementary pattern forming the lattice. Bloch theory provides a classical framework for investigating wave
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating