[Next-Generation Sequencing Book]
  • [Photo] Song Li • January 8, 2015

    BACKGROUND

    The laboratory of Dr. Song Li invites applications for a post-doc position in the College of Agriculture and Life Sciences at Virginia Polytechnic and State University. Li lab focuses on developing computational algorithms that integrate large-scale data to address key questions in plant biology. The lab has the following ongoing projects: 1) Improving the statistical methods for the identification of alternative splicing variants and antisense transcripts using RNA-seq in diverse species. Developing co-expression network analysis tools that incorporate splicing information. 2) Developing machine learning methods that identify active regulatory networks controlling cell type- or condition-specific gene expression. 3) Developing Hidden Markov Model (HMM) based gene prediction method that incorporates diverse genomic data and evolutionary conservation to improve gene and splicing variant discovery and annotation in both model species and crop species.

    RESPONSIBILITIES

    The successful candidate will perform computational analysis of next generation sequencing data using HPC clusters. The postdoctoral associate will be part of Crop and Soil Environmental Sciences Department and will work in a multidisciplinary environment and have opportunity to collaborate with faculty in the Departments of Computer Science, Biological Sciences, Biochemistry, and the Virginia Bioinformatics Institute. The postdoctoral associate will be expected to contribute significantly to the research projects in the lab, to prepare the results for publication and presentation, to help supervise graduate and undergraduate students, and to contribute to grant proposals.

    REQUIREMENTS

    • Ph.D in Bioinformatics, Computational Biology, Computer Science, Applied Mathematics or other related field
    • Track record of publications in bioinformatics, computational biology, genomics or systems biology
    • Strong programming skills in Python, Perl, Java, C++ or other language
    • Familiar with data analysis and visualization using R
    • Experience with Linux and high performance computing environment
    • Demonstrated ability in developing novel statistical or machine learning methods in computational biology
    • Experience in genome scale data analysis such as analysis of microarray, ChIP-Seq and RNA-Seq data, network analysis, biological sequence analysis or other relevant computational genomics experience
    • Highly motivated for interdisciplinary research, excellent communication skills, and the ability to work independently as well as within a research group

    HOW TO APPLY

    Initial appointment is one year, with possible extension depending on performance. The interested applicants should send their C.V., available date, and names of three references to songli[at]vt.edu.

    DEADLINE

    Review of applications will begin January 25th 2015.

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