Postdoc positions for cancer genetics, cancer cell biology/immunobiology, or computational genomics

Employer
Wang lab at University of Pittsburgh Cancer Institute
Location
Pittsburgh, Pennsylvania
Salary
Based on experience
Closing date
Nov 16, 2023

Two postdoctoral positions are available at Wang Laboratory of the UPMC Hillman Cancer Center, in the Department of Pathology, University of Pittsburgh. The individuals will be working on our leading-edge integrative genomics initiatives aimed at identifying pathological genetic aberrations and immunological targets from multi-dimensional cancer genomics datasets, as well as developing mechanism-driven computational models for precision oncology. The candidate will receive unique multidisciplinary training on cancer genetics, cancer cell biology, immunobiology, computational genomics, and translational research. The candidate must hold a Ph.D. degree. Preferences will be given to highly motivated candidates with an outstanding publication record in cancer research or bioinformatics, and those with established expertise in cancer cell biology, immunobiology, or computational genomics.

The research project for the wet lab position will interface the “dark side” of cancer genetics with cancer pathobiology and immunobiology. Specifically, the candidate will investigate a cryptic class of adjacent gene rearrangements in more aggressive and therapy-resistant forms of breast and/or ovarian cancers, and examine their function in cancer progression and/or immunotherapy resistance. The primary responsibilities will include experimental validation of newly discovered genetic targets, characterize their molecular basis, elucidating their clinical significance using patient samples, confirming their role in tumor progression, immune disfunction, or therapy resistance in vitro and/or in vivo, pinpointing their mechanistic basis, and exploring potential clinical applications.

The research project for the dry lab position will focus on developing mechanism-driven computational models for predicting therapeutic responses of cancer targeted therapies and immunotherapies. Our lab pioneered a new class of multi-omics modeling methods called “integral genomic signature analysis” for genomics-based precision oncology (Nature Communications 2022). Specifically, the candidate will implement advanced algorithms and machine learning methods grounded on biological mechanisms to develop clinical-grade predictive models for cancer targeted therapies and immunotherapies. The candidate may also work on characterizing the landscape of structural mutations in cancer and their functions in dictating the tumor immune microenvironment and immunotherapy responses.

Wang laboratory is a translational cancer research lab at UPMC Hillman Cancer Center, Department of Pathology, University of Pittsburgh. Our lab is dedicated to the discovery and characterization of novel breast cancer genetic targets from multi-dimensional genomic datasets to achieve precision therapeutics. Our research projects have been funded by three awards from NIH/NCI, five awards from the Department of Defense, two awards from Susan G. Komen Foundation, as well as Nancy Owens Breast Cancer Foundation, Commonwealth of PA, and PA breast cancer coalition, and our studies have led to high-profile publications in Nature Biotechnology, Nature Communications, PNAS, Cancer Discovery, Cancer Research, and Clinical Cancer Research, etc. Our postdoc trainees have received four prestigious postdoc fellowship Awards including two awards from Susan G. Komen Foundation and two awards from the Department of Defense.

As our major achievements, our lab has identified two recurrent gene fusions ESR1-CCDC170 and BCL2L14-ETV6 in luminal B and triple-negative breast cancer, respectively -- the only two canonical gene fusions identified in major breast cancer entities to date. In addition, our lab has characterized two breast cancer kinase targets, NLK and TLK2, and preformed preclinical studies of their inhibitors. Our computational genomics research focuses on precision oncology and immuno-oncology. We have developed an innovative integral genomic signature approach for tailored cancer therapy using genome-wide sequencing data. We also developed an integrated HEPA-PARSE approach for high-throughput identification of tumor associated antigen targets. Our research projects are backed up with topnotch research facilities, vast clinical sample repositories, and first-class computational infrastructure at UPMC and University of Pittsburgh. For more information, please visit https://cagenome.org/lab

To apply, please use the application form below and upload a combined pdf file containing your CV, cover letter, and the contact information of 3 referees. Review of applications will begin immediately until these positions are filled. The University of Pittsburgh offers a comprehensive salary program and outstanding benefits in a smoke and drug free workplace. University of Pittsburgh School of Medicine is an Affirmative Action/Equal Opportunity Employer and encourages applications from under-represented groups.

Xiaosong Wang, M.D., Ph.D.

Associate Professor of Pathology

UPMC Hillman Cancer Center

Email: xiaosongw@pitt.edu

Apply for Postdoc positions for cancer genetics, cancer cell biology/immunobiology, or computational genomics

Fields marked with an asterisk (*) are required

Your file must be a .doc, .pdf, .docx, or .rtf. No larger than 1MB
Selected file:

Supplementary Documents

Your file must be a .doc, .pdf, .docx, or .rtf. No larger than 1MB
Selected file:
Your communication preferences

When you apply for a job we will send your application to the named employer, who may contact you. By applying for a job listed on Science Careers you agree to our terms and conditions and privacy policy. You should never be required to provide bank account details. If you are, please contact us. All emails will contain a link in the footer to enable you to unsubscribe at any time.

Get job alerts

Create a job alert and receive personalized job recommendations straight to your inbox.

Create alert