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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 17 hours ago
Position Information General Information Position Number 009668 Vacancy Open to All Candidates Working Title Facility Planner - Mobility Position Designation EHRA Non-Faculty Employment Type
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-dependent MLCS regulation on lysosomal trafficking, mitochondrial quality control and hypoxia-induced cell death. Please apply for this project using this link: https://www.sheffield.ac.uk/postgraduate/phd
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Single-cell high-throughput sequencing technologies generate unprecedented volumes of molecular data at cellular resolution, opening new avenues for the application of machine learning
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pathways that regulate cardiac organ development and homeostasis. Our lab performs computational and wet-lab work, though the ideal candidate should have experience in molecular biology, genomics, cell
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applications for a fully funded postdoctoral associate position. This position, available immediately, focuses on developing machine learning and deep learning methods for analyzing large-scale single-cell DNA
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critical insights into the sex differences in ageing of blood vessels in bone and its functional consequences. We have developed numerous methods, including advanced 3D imaging, cell-specific inducible mouse
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for Integrative genomics team at the Institut Pasteur as part of the ERC Starting Grant MULTIview-CELL, will be working on the development of a virtual tissue model bridging cell-cell communication and gene-gene
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to biologically meaningful outcomes. The successful candidate will contribute to the application, evaluation, and interpretation of AI models on single-cell biological datasets, while keeping downstream translation
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) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 10 May 2026 - 22:00 (UTC) Country France Type of Contract Temporary Job Status Full-time Is the job funded through the EU
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. The successful candidate will apply and develop bioinformatics, statistical, machine learning, and deep learning approaches to analyze single-cell sequencing and spatial expression data, including single-cell RNA