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Bioinformatics/Machine Learning/Biomarker Discovery Postdoctoral Fellow
Company Information
Dr. Xiaoqing Yu’s Lab, within the Department of Biostatistics and Bioinformatics at the H. Lee Moffitt Cancer Center, is seeking a highly motivated, independent, and collaborative postdoctoral research fellow interested in conducting exciting inter-disciplinary projects in omics data analysis and cancer biomarker discover. The post-doctoral research fellow will also be co-mentored by Drs. Jamie Teer, Xuefeng Wang. The positions will remain open until filled.
Position Highlights:
• Work on unique, multi-omics datasets to support translational lung cancer research.
• Develop cutting-edge methods for processing, analyzing, and interpreting multi-omics data.
• The position is essential for development of comprehensive tools benefiting basic to clinical research through identification of novel biomarkers to treat cancer and the improved treatment stratification of cancer patients.
• The postdoctoral research fellow will enjoy a close working relationship with bioinformaticians, statisticians, clinicians, translational researchers, as well as bench scientists to establish a well-rounded multidisciplinary team.
• Moffitt integrates excellent patient care with cutting-edge clinical and basic research. Having a cancer hospital on-site provides unique opportunities for translational research.
• Outstanding mentorship from expert faculty with wide-ranging funded research programs including T32 Training Grants.
• You will be encouraged and supported to apply for training grants.
Responsibilities:
• Develop bioinformatics pipelines for analyzing high dimensional immunological data.
• Develop novel visualization tools for interpreting big cancer Omics data.
• Discover new predictive biomarkers for cancer prognosis.
• Develop and extend machine learning methods for big cancer Omics data such as single-cell omics data and spatial transcriptomics data.
• Supports the full life-cycle of omics data: experimental design, data quality control, bioinformatics analysis, biological interpretation of results, and delivery.
• Collaborate on a variety of cancer and bioinformatics research projects.
The Ideal Candidate:
• Experience of analysis and integration of multi-omics data (e.g., DNA-seq, RNA-seq, proteomics).
• Research background/experiences in Bioinformatics, Statistics, Computational Biology, or Machine Learning.
• A highly motivated and independent researcher with a strong quantitative scientific background.
• Programming experience in statistical program R and/or scripting languages such as Perl/Python on Unix/Linux systems.
• Experience in cancer genomics is not required but the ability to learn new knowledge is highly desirable.
• Excellent communication and writing skills.
• Experience of single-cell RNAseq, spatial transcriptome data, is preferred.
Credentials and Qualifications:
PhD in Bioinformatics, Statistics/Biostatistics, Computational Biology, Data Science, Machine Learning, or related fields.