The Department of Biostatistics at UT MDACC has one post-doc position open for biostatistics methodology research and high-throughput data analysis applications. The focus is research and publication. The primary research area can encompass: (i) developing and applying novel statistical and computational methods for the analysis of omics data from various sources such as high-throughput proteomic, genomic, sequencing, transcriptomic, and imaging data, with particular emphasis on developing integrative and flexible models that incorporate both biological knowledge and empirical structures; (ii) developing novel statistical and machine learning methods for the analysis of proteomics data with focuses on cancer research. Specific research areas include the integration of metabolites and proteins for early detection and risk assessment of cancer.
The fellow will gain experience in the cutting-edge analysis of "big data" and have the opportunity to publish in high-impact biostatistics, bioinformatics, and health research journals. The post-doc will work under the supervision of Drs. Ehsan Irajizad, Kim-Anh Do, and Sam Hanash on challenging and important clinical and biological projects that involve complex statistical modeling, data analysis, and computation.
The candidate will learn in areas including statistical theory and application in cancer proteomics/genomics; Obtain expertise in the integration of metabolites and proteins for early detection and risk assessment of cancer; Gain experience in the cutting-edge analysis of "big data" and novel machine learning algorithms; Acquire extensive experience in R, R-Shiny, Python, and other programming languages/environments.
We seek a highly motivated individual with a Ph.D. in biostatistics/bioinformatics or a related quantitative field such as computer science, engineering, or quantitative computational biology. Interest or background in computer-intensive methodology, bioinformatics, genomics, and proteomics is preferred.
Applicants must have strong training in statistics and excellent programming skills, in particular, R/Python/Matlab and preferably one lower-level computer language such as C or Fortran, and interest in the application of state-of-the-art statistical methods to complex data. Experience with high-performance computing and Linux system is a plus.
Expertise or skills in any of the following areas are desirable: analysis of high-dimensional data with missing values, mass-spectrometry, and biomedical data analysis.