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infected human cells, identify viral and host complexes in their native context, and combine this with structural modeling and proteomics to build a spatially and temporally resolved picture of the infection
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will contribute to a spatialized, dynamic life cycle assessment of ruminant production systems in the Northeast U.S. using integrated assessment models. The candidate will also contribute
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predict cancer progression. We use a combination of experimental and clinical data paired with computational analyses to model tumor evolution. We seek a postdoctoral candidate who will apply a combination
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position within a Research Infrastructure? No Offer Description Area of research: Scientific / postdoctoral posts Job description:Postdoc / Modelling and Analysis of Integrated Power, Gas and Hydrogen
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to answer: How temporal and structural sparsity can be incorporated into the 3D dynamics reconstruction model? How sparse can a measurement be and still capture a high-resolution dynamic event? How do
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expression, clustering, dimension reduction, pathway analysis, survival analysis, and multivariable modeling. Strong analytical, organizational, and problem-solving skills. Ability to work independently while
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-BiFC knockin mouse models to 1) determine how PDGF ligand identity and concentration affect receptor dimerization and internalization using flow cytometry and CITE-seq in combination with spatial
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or equivalent Skills/Qualifications Technical Skills: Hydrological analysis and modeling. Spatial analysis using GIS. GIS software (QGIS, ArcGIS). Hydrological modeling software. Processing and analysis
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to answer: How temporal and structural sparsity can be incorporated into the 3D dynamics reconstruction model? How sparse can a measurement be and still capture a high-resolution dynamic event? How do
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program seeks to increase our understanding of the immunological and molecular mechanisms involved in autoimmune dysregulation in Type 1 diabetes (T1D). Through the use of novel murine models of T1D, human