Postdoctoral Fellow for Bioinformatics, Machine Learning and Biomarker Discovery
At Moffitt Cancer Center, we come face-to-face with cancer every day, but we also see courage. And it inspires us to be the safest and best place for cancer care – to bring greater hope to every patient we serve. It’s why we’ve been continually named One of the Top Places to Work in the Tampa Bay Area. As the only National Cancer Institute-designated Comprehensive Cancer Center based in Florida, Moffitt employs some of the best and brightest minds from around the world. Moffitt is the leading cancer hospital in both Florida and the Southeast and has been nationally ranked by U.S. News & World Report since 1999. Because working at Moffitt is both a career and a mission: to contribute to the prevention and cure of cancer. Join a dedicated, diverse and inclusive team of over 7,000 to be a part of the Courageous future we envision.
Summary
Position Highlights:
Dr. Xuefeng Wang’s Lab (lab.moffitt.org/wang), at H. Lee Moffitt Cancer Center, an NCI Comprehensive Cancer Center and top-ranked cancer research insitute, is seeking one new postdoc fellow to join the group and work on an exciting project funded by NIH. The position will remain open until filled (earlist start date of April 1 2021).
The Ideal Candidate:
• Research background/experiences in bioifnormatics, computational biology, or Statistics.
• A highly motivated and independent researcher with a strong quantitative scientific background and programming skills.
• Experience in cancer genomics in not required but ability to learn new knowledge is highly desirable.
Responsibilities:
• Will develop and extend machine learning methods for big cancer Omics data.
• Will help discover new predictive biomarkers for cancer prognosis.
• Will be responsible for developing novel visualizing too that will facilate interpreting big Omics data in cancer research.
Credentials and Qualifications:
Required: PhD in Bioinformatics, Statistics/Biostatistics, Data Science, Computer Science, or related fields.
Preferred:
• Strong programming skills in languages such as R/Python.
• Research background in machine learning.
• Experience of integration of multi-omics data including whole exome sequencing, RNA-seq, metabolomics, proteomics, etc.
• Excellent communication and writing skills.