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analyses of single-cell and spatial omics to advance precision medicine. Dr Chen’s research team develops AI algorithms for spatial and single-cell omics, integrates publicly available datasets into a
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. This postdoctoral fellowship in the Arlotta lab involves computational analysis of large, multimodal single-cell datasets, as part of a collaborative project which also aims to generate AI and machine learning models
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immunity, or animal models of chronic inflammation Expertise in advanced immune phenotyping (e.g. spatial omics, single‑cell omics including deep learning, O-link proteomics, Crispr screening, human PBMC
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of Ophthalmology. Our research focus is retinal cell biology with the goal of understanding the fundamental processes of vision and discovering small molecule therapy for photoreceptor degenerative disorders
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single-cell and single-nucleus multi-omics, machine learning/AI, computational biology, and experimental validation to understand cardiovascular disease progression and identify novel therapeutic targets
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-doctoral candidate will be involved in both in vitro and in vivo research using animal models, primary, and immortalized cell culture models, molecular and cellular techniques, immunofluorescence, and
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research integrates fundamental immunology, functional genomics, and translational cell engineering to uncover mechanisms of immune regulation and translate these discoveries into next-generation
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, regeneration, and function, with a particular emphasis on thymus biology, hematopoietic stem cell transplantation, immune reconstitution, tolerance, and graft-versus-host disease. The Thymus Research Team
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publication list Two educational or professional recommendations All documents must be in English or include an official English translation. Connect with ORISE...on the GO! Download the new ORISE GO mobile
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that establish pancreatic beta-cell identity also determine lifelong resilience or vulnerability to disease. Current projects investigate how disrupted DNA methylation and chromatin regulation can drive genomic