Employment Announcement

We are looking for Ph.D. students and a postdoctoral trainee to join our group and work with multidisciplinary groups (University of Maryland, NIH, USDA and FDA) that include Statisticians, Applied Mathematics, Epigenetics, Immunologists and Animal Genetics.

Qualifications: Ph.D candidates should be highly motivated and have an interest in statistical genetics and computational biology. Ability to effectively communicate in English is essential. A MSc degree or equivalent in Statistical Genetics, Applied mathematics, Biophysics, Biostatistics, and is strongly preferred. Experience with programming in R, Perl, SAS, C++ and/or matlab, a plus. The position is for postdoctoral trainee with doctoral degrees and some prior training or expertise in the statistical genetics, mathematical, or computational sciences who seek training at the interface between statistical and molecular genetics. Strong programming in R, Perl, SAS, C++ and/or Matlab, a plus.
Topics:
1) Adaptation and application of advanced mathematical methods to the analysis of high- throughput genomic and epigenomic data.
2) Development of computational epigenetics methods/tools.

Applications should consist of a letter of interest containing addresses and telephone numbers; names of three of references, a CV with publication record and/or thesis topic, and a one- page statement of interest for postdoctoral trainee. Please submit queries and applications.

Research - Statistical Genomics and Bioinformatics

The current research interests are on bioinformatics, statistical genomics, biopathway analysis and gene regulatory network. Specifically, he works in novel computational methodologies for molecular biology and genetics, e.g., temporal gene expression analysis and biological information extraction from high throughput gene expression data.

Teaching

• BIOM 603 Applied Regression Analysis
• Statistical Genomics and Bioinformatics

Publications

Representative Publications

• Song, J., Jaime Bjarnason and Mike Surette. 2005. The Identification of Functional
Motifs with Longitudinal Mixed Model and False Discovery Rate (FDR) in Temporal Gene Expression Analysis. Evolution Bioinformatics(Accepted).
• Song, J., Tony Ware, Shu-lin, Liu M. Surette 2004 Comparative genomics for closely-related strains by Wavelet Analysis in Bacteria. EURASP Applied Signal Processing (Genome issue), 2004:1, 5-12.
• Song, J., Tony Ware, Shu-lin, Liu, Surette M. 2003 Identification and Test of Origin Site and Terminus Site of Replication by Nonparameters Method. METMBS 2003 17-21 CSREA Press.
• Song, J., Antony Ware and Shu-Lin Liu 2003, Wavelet to predict bacterial ori and ter:a tendency towards a physical balance. BMC Genomics 2003 4:17.
• Song, J., D. L. Koller, T. Foroud, K. Carr, J. Zhao, J. Rice, J. I.Nurnberger Jr., H.Begleiter, B. Porjesz, T. L. Smith, M. A. Schuckit, H. J. Edenberg (2003) Association of GABAA receptors and alcohol dependence and the effects of genetic imprinting. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 117B, 39-45