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, antigen presentation and trained immunity) using mouse models that enable CPT1A gain or loss of function. Furthermore, the bioenergetic profile of macrophages both in vitro and ex vivo will be assessed
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acquire advanced expertise in navigation models, spatial representation, object representation, and relational knowledge representation, as well as in planning algorithms based on probabilistic models and
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques
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driving sex-specificity in tumorigenesis, organismal development, and spatial biology of the liver. To do so, we intersect large-scale genetic analysis utilizing experimental models with spatial and single
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on inverse multiple-scattering algorithms. Implement and evaluate both linear approximation models and nonlinear high-order scattering approaches for accurate imaging of thick and strongly scattering
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such as R, SAS, ArcGIS, SQL, Python and AI tools. Conducts geospatial and epidemiologic analyses relevant to the catchment area to assess cancer outcomes, spatial patterns, temporal trends, and disparities
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, and spatial organization, crucial especially in early developmental stages but relevant throughout life. Drawing from cellular and molecular biology, it delves into embryology, morphology, genetics
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for single-cell and spatial omics Deep learning and representation learning to model cellular states and interactions Explainable AI for biomarker discovery and patient stratification Cross-disease modeling
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solve research challenges and model development, as applicable. ● Contribute discrete components of a larger project under the general direction of a senior or principal researcher. ● Prepare complete
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, the following non-mandatory skills will be positively considered: have experience in the modelling of the relation between environmental exposures and human health, e.g. distributed lag nonlinear models, spatial