Post Doc Res Assoc w/Ret

Post Doc Res Assoc w/Ret

Job Summary

The Marth laboratory (http://marthlab.org) is seeking postdoctoral scientists with an interest in computational tool development and biomedical data analysis to join our team in three research areas.

(1) Machine learning algorithms for anticancer therapy selection. We are looking for a postdoctoral researcher to develop novel machine learning approaches for driving computational selection of targeted- and chemotherapies in a clinical precision oncology context. This work will leverage an active, NCI-funded research program in which we collaborate with cancer biologists, pharmacologists, and clinical oncologists in advanced/metastatic breast cancer and brain cancer; and utilize unique pharmaco-omic (drug response) datasets from patient-derived models of cancer available at the University of Utah and Huntsman Cancer Institute. The ultimate goal is to evaluate these approaches in clinical trials and implement them in the clinic for personalized therapy selection in treatment-refractory cancer patients.

(2) Computational algorithms for somatic mosaicism discovery. We are also looking for a postdoc to participate in the development, implementation, benchmarking, and application of reference-free computational algorithms for detecting somatic mutations. This work is supported by a grant award in Dr. Marth's laboratory to participate in the Somatic Mosaicism across Human Tissues (SMaHT) Network, an NIH initiative to transform our understanding of how somatic mosaicism in organs, tissues, and cells influences human biology and disease (https://commonfund.nih.gov/smaht). The SMaHT Consortium is generating a wide spectrum of sequencing datasets from human tissues, providing a unique opportunity for discovery in a highly collaborative project involving >20 research groups and institutions. In this project, we are contributing to the construction of a comprehensive Healthy Human Somatic Mosaicism Atlas across multiple tissues in hundreds of donors.

(3) Machine learning algorithms for rare disease discovery and diagnostics. We have a postdoc opening to develop machine learning approaches to study large cohorts of rare disease patients with extensive phenotype and genotype data. As part of the NIH-funded Undiagnosed Diseases Network (UDN) Data Management and Coordination Center, the Marth Laboratory is building a comprehensive rare-disease patient data management platform and a large set of diagnostic tools for improving diagnostic rates in the UDN patient cohort. Most relevantly, we are developing phenotype-driven patient-matching approaches that leverage the UDN patients and additional large disease cohorts to identify groups of patients with similar phenotypes to identify shared causative genes within the group. The long-term goal of this project is to develop advanced AI/ML algorithms for effective patient clustering, and to build a large database of diagnosed and as yet undiagnosed rare disease patients in which these algorithms are applied to significantly move the needle on improving diagnostic rates.

The ideal candidate will have obtained a doctoral degree in computer science, engineering, bioinformatics, or genetics; have proficiency with computer programming (C++, Python); experience with algorithm development; and familiarity with the UNIX operating system. Background in genetics and in AI/ML algorithm development, and experience with biomedical data analysis are a big plus. This position is available immediately.

Please contact Dr. Gabor Marth at gmarth@genetics.utah.edu for any additional information or to apply for one of the postdoc positions.



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Requisition Number: PRN40550B
Full Time or Part Time? Full Time
Work Schedule Summary:
Department: 00246 - Human Genetics
Location: Campus
Pay Rate Range: DOE
Close Date: 1/30/2025
Open Until Filled:

To apply, visit https://utah.peopleadmin.com/postings/174891


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