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Field
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-omic, multi-context gene regulatory networks (GRNs) from large-scale single-cell datasets. We are pioneers in GRN reconstruction from single-cell multi-omics, including: Causal GRNs from Perturb-seq
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University of Southern California (USC) | Los Angeles, California | United States | about 2 hours ago
responsibilities for this role include: Initiating, planning, and overseeing research projects Leading the analysis of complex multiomics data, including single-cell and spatial omics, using R or Python; experience
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technologies for AC/DC hybrid power systems—alternating current networks that integrate DC networks—and the definition of functional requirements to ensure their performance and reliability. As part of
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emphasize the different kind or levels, known as layers, of connections between the elements of the network and the interactions between these levels as well. In order to capture the structure and complexity
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techniques to enhance MT use, thus improving the translator's working environment and job sustainability, as well as the user experience. To better understand this complex process, INCREC triangulates data
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addressing complex challenges. Responsibilities will include writing reports, authoring publications, drafting patents, and presenting results at conferences. The ideal candidate will possess demonstrated
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of modeling higher-order relational structures such as hypergraphs and simplicial complexes, which are prevalent in complex biological systems like protein assemblies, signaling pathways, and metabolic networks
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analysis techniques, including space syntax, isovist measures, and visual complexity assessments. The successful candidate will work closely with researchers at Cambridge and ETH Zurich to quantify spatial
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address complex environmental challenges. About the Project The Adaptive Design for AI-Driven Processes in Transforming Dynamic Landscapes (ADAPT) project develops scalable, data-driven design methodologies
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dozen input variables). The foreseen approach would be to build on recent developments in using CNNs for Species Distribution Models (e.g. Deneu et al 2021, Morand et al 2024) to summarise the complex