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Statistical Genomics Postdoctoral Research Fellow
Company Information
The Bioinformatics and Computational Biology (BCB) department at MD Anderson Cancer Center is a hub of innovation and discovery, dedicated to advancing cancer research through cutting-edge computational and data-driven approaches. By integrating bioinformatics, systems biology, and advanced computational techniques, the department empowers researchers and clinicians to unravel the complexities of cancer biology, from understanding molecular mechanisms to predicting patient outcomes. With a multidisciplinary team of experts, state-of-the-art infrastructure, and a focus on collaboration, the BCB department supports the development of novel algorithms, pipelines, and tools that enable the analysis of high-throughput genomic, proteomic, and clinical data. By bridging the gap between data science and cancer biology, the department plays a pivotal role in fostering personalized medicine, accelerating discoveries, and improving cancer care for patients worldwide.
Dr. Ye Zheng's lab works on problems at the interface of statistical, biological and biomedical sciences. The lab has developed methods to decipher gene cis-regulatory mechanisms from transcriptomics, epigenomics, proteomics and three-dimensional (3D) chromatin interaction perspectives. We are seeking a highly motivated and dedicated postdoctoral researcher to join our dynamic, hybrid, and highly collaborative lab. This statistical genomics postdoctoral fellow candidate is expected to leverage the single-cell and bulk-cell multi-omics data to reveal the cancer-specific mechanism that underlies the differential efficacies and toxicities of treatment across patients. This position offers an exciting opportunity to contribute to pioneering biological and clinically important and methodologically challenging problems by innovating cutting-edge statistical models and computational methods. This position provides extensive training in grant writing, with a focus on prestigious early career development grants such as the K99 and Damon Runyon awards.
LEARNING OBJECTIVES
Statistical modeling and computational pipeline development:
• Conduct raw sequencing data processing and quality control analysis
• Conduct standard data analysis according to the computational pipeline
• Conduct customized preliminary data analysis in a case-by-case manner
• Develop new statistical models, leverage and customize the modern machine learning algorithm, and construct computational tools or pipelines to better analyze the data for the specific scientific target
• Draft manuscript and contribute to the PI's grant preparation
• Present research work at weekly meetings, domestic or international conferences
Grant and general lab management:
• Prepare grant applications eligible to postdoctoral researchers
• Aids and supports the PI in the research and computational pipeline maintenance and organization efforts
• Train students and new members in research procedures and in the use of computational resources
• Participate and help other lab members with ongoing projects in the lab
Individuals with a PhD degree or soon-to-be Ph.D. graduate in Statistics, Biostatistics, Bioinformatics, Computational Biology, Computer Science, Engineering, Data Science, or a related field are encouraged to apply.
1. Solid training in Statistics:
Past course or research training in statistics, including but not limited to mathematical statistics, statistical inference, and linear regression.
2. Strong computational skills:
• Proficient in programming languages R, Python, and Shell, has extensive usage of high-performance computing environments how to submit batch run jobs.
• Experienced in processing and analyzing bulk and single-cell genomic data.
• Experienced in developing computational tools, such as R package, Python module, interactive webpage, and computational pipelines.
• Ability to conduct highly organized and reproducible research.
3. Strong communication, writing, and collaboration ability.