Translational systems biology postdoc position [multi-scale patient models informed by organoids]

[Emily Miraldi]

Dear Colleagues,

I have an open computational postdoc position in my lab, to build mutually-informative in silico and organoid models of gastrointestinal diseases for prediction of patient-specific responses. The position revolves around analysis of multi-'omic, single-cell datasets generated from patient biopsies and organoids (more details below my signature).

I'd appreciate your help circulating this message. Interested candidates can reach out to me directly with CV and letter of intent.

Thank you in advance,

Emily Miraldi

Associate Professor of Immunobiology and Biomedical Informatics

Cincinnati Children's Hospital

miraldilab.cchmc.org

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Recruiting a postdoc to help build a UM1 Center:

Patient-specific, combinatorial NAMs for gastrointestinal diseases and drug response prediction

Millions of people in the US are impacted by gastrointestinal diseases including Inflammatory Bowel Disease (IBD), Metabolic Disfunction Associated Steatotic Liver Disease (MASLD) and Pancreatitis. There are only a small number of drugs for IBD and MASLD, and none for Pancreatitis. Animal models have proven inadequate surrogates for these diseases and reliance on current preclinical evaluations are considered to be among the most problematic steps in drug discovery.

The goal of Cincinnati Advanced NAM Development and Operational Research center (CANDOR) is to develop combinatorial New Approach Methodologies (NAMs) that more accurately model the pathophysiologic complexity and drug responses in patients with these gastrointestinal (GI) diseases.

The aims of CANDOR are: 

1. to establish in vitro NAMs (tissue organoids derived from patient induced pluripotent stem cells) that accurately model clinical features of IBD, MASLD, and Pancreatitis;
2. to build disease-focused in silico NAMs that are based on integration across multiple modeling and patient-derived data types (gene regulatory and cell-cell communication networks from sn-multiome-seq and spatial transcriptomics of in vivo patient biopsies + in vitro organoids; genomics data also inform pharmacometrics and artificial intelligence modeling of patient responses from clinical data, polygenic risk scores and histology);
3. to iteratively refine in vitro and in silico NAMs; and 
4. to validate and disseminate combinatorial NAM technologies through training, outreach, and distribution.