Find Jobs
Find Jobs Near You – Available Work in Your Location
Skip to job details
CH
CO2000 H. Lee Moffitt Cancer Center and Research Institute, Inc.
APPLIED POST DOC FELLOW
Career Insights for Computational Biologist
See where this job fits in the broader career landscape. Knowing your career path helps you see what's possible from here.
Scorecard
Based on Arkansas data
Review key factors to help you decide if this role fits your goals. How is this calculated?
What they do
A Computational Biologist uses biological data to develop models to better understand biological systems. Conducts analysis using computational and mathematical methods and large data sets.
$108,196 / year median in Arkansas
+4% projected growth
Job Description
Working at Moffitt is both a career and a mission: to contribute to the prevention and cure of cancer. 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. Join a dedicated team of nearly 11,000 who are shaping the future we envision. Moffitt has been recognized as a Best and Brightest Company to Work For in the Nation and is continually named one of the Tampa Bay Times' Top Workplaces. Summary About the Lab Omics techniques emerged as efficient and comprehensive approaches to profile bio-signatures in many biomedical fields at the resolution of cell molecules and tissue imaging pixels. Quantifying and interpreting these signatures from omics profiling face data challenges such as heterogeneities, noise/errors, large volume, context complexities, etc. Our lab (https://lab.moffitt.org/teng) in the Department of Biostatistics and Bioinformatics focus on developing statistical and AI methods to analyze, annotate and integrate complex omics datasets. We build open-source software to ease the data analysis across omics modalities. Particularly, our ongoing work in the lab addresses questions including: heterogeneity removal in genomic sequencing data, enhancer function dissection with large epigenomic data modeling, DNA oncovirus mechanisms with cross-modality integration, etc. Beyond ongoing focuses, we are also expanding the research in other topics: statistical modeling in spatial omics; AI models in translational pathology; AI modeling of clonal hematopoiesis evolution, etc. We are commited to understanding human diseases like cancers through data science innovations. We are looking for a Postdoctoral Fellow in AI for omics to contribute to any of the topics above.
Ideal Postdoc Candidate:
- Highly motivated and passionate about biomedical data science
- Strong background in deep learning and/or foundation models
- Proficient programming and scientific writing
- Experienced in omics data processing
- Excellent comminucation skills
Minimum Qualifications:
- A Ph.