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will expand the Rosaceae, Citrus, Vaccinium and Pulse crop databases to meet community demand for resources with usable big data aggregated, analyzed, integrated, and visualized, as well as data
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targets. You will take a lead on development of computational approaches to integrate multi-omics data from patient samples, including DNA methylation, histone modifications, single-cell transcriptomics and
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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analyst for an ARC South London study on multi-morbidity in children using CPRD data. The ARC South London Children’s and Young person theme is important as many children in south London experience high
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software development, and has leading roles in the ATLAS High Luminosity Large-Hadron Collider (HL-LHC) upgrade (Silicon Strip detector, Liquid Argon calorimeter, and Trigger & Data Acquisition). The group
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models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science
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fully resolved single-fracture simulations to large-scale discrete fracture network (DFN) upscaling. Your work will directly address high-value industry challenges in energy and resource systems. CML is
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direction of Hannah Druckenmiller to conduct research in environmental economics and policy analysis. The group’s research involves using quasi-experimental methods and large, geospatial data to analyze
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large animal models (pigs). Works with collaborators to develop preliminary data and analyze animal model outcomes. Develops tissue culture assays to support research goals and isolation of primary cells
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The Princeton University WET LAB (https://ren.princeton.edu/) is seeking a postdoctoral research associate(s) or more senior researcher(s) with expertise and interest in Large Language Models (LLM