H. Lee Moffitt Cancer Center & Research Institute
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We are hiring an Applied Bioinformatics Research Scientist to identify metabolic vulnerabilities in lung cancer for therapeutic targeting. Lung Cancer is the most common cause of cancer deaths worldwide. Tyrosine kinase inhibitors and immunotherapy have been shown to be effective in a subset of patients; however, the overall survival rate for this disease remains low, especially for metastatic disease. Moreover, small cell lung cancer (SCLC) patients have a poor prognosis, and there especially exists a gap in knowledge in understanding SCLC and identifying effective therapeutic strategies. The goal of our research team is to understand the underlying biology of key drivers in lung cancer by identifying metabolic vulnerabilities that can ultimately be used as single agents or combined with immunotherapy to target lung cancer therapeutically. Join our team of researchers who are funded through a P01 Program Project Grant!
To learn more, click on the following link – https://www.moffitt.org/research-science/research-teams/lung-cancer-metabolism-working-group/faculty/
Position Highlights:
• Work on unique, multi-omics datasets to support translational lung cancer metabolism research.
• Develop cutting-edge methods for processing, analyzing, and interpreting multi-omics data.
• Enjoy a close working relationship with bioinformaticians, statisticians, bench scientists, and translational researchers to establish a well-rounded multidisciplinary team.
• Join an institute that highly values computational research.
The Ideal Candidate:
• Experience working with at least one “omics” type (e.g., DNA-seq, RNA-seq, metabolomics, proteomics).
• Preference will be given to candidates with experience in integration of multi-omics data.
• Programming experience in statistical program R and/or scripting languages such as Python/Perl on Unix/Linux systems.
• Highly motivated and independent researcher with a background in cancer biology.
• Excellent communication and writing skills.
Responsibilities:
• Supports the full life cycle of omics data: experimental design, data quality control, differential expression analysis, biological interpretation of results, and delivery.
• Ensures timely and proper analysis of omics data generated by the team.
• Maintains or develops new functionality for bioinformatics pipelines to analyze and visualize multi-omics data.
• Collaborates on a variety of cancer and bioinformatics research projects.
Credentials and Qualifications:
• PhD in Bioinformatics, Data Science, Computational Biology, Machine Learning, Statistics/Biostatistics, or related fields.
Moffitt Cancer Center is the only NCI Designated Comprehensive Cancer Center in Tampa, Florida. Tampa is an ideal choice for those seeking opportunities in a rapidly growing metropolitan city, which has become a hub for ground-breaking research. With its vibrant economy and diverse culture, Tampa Bay offers a unique blend of urban sophistication and natural beauty. Join the many who have already discovered what makes Tampa one of the fastest-growing metropolitan cities in the US.
If you have the vision, passion, and dedication to contribute to our mission,
then we have a place for you.
How to Apply: Interested applicants should send a single PDF file that includes a cover letter summarizing their research training and accomplishments, a personal statement of scientific interests and goals, a current CV with recent publications, and contact information for three references to Drs. Xiaoqing Yu ([email protected]) and Paul Stewart ([email protected]).
Equal Employment Opportunity
Moffitt is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, or protected veteran or disabled status. Moffitt seeks candidates whose skills, and personal and professional experience, have prepared them to contribute to our commitment to diversity and excellence.
Apply now
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