PhD assistantships - Experimental design and statistical analysis of
transcriptional profiling experiments.
Statistical Genomics Laboratory, Departments of Animal Science and
Fisheries and Wildlife, Michigan State University.
We are looking for students to join our group and work towards a doctoral
degree while collaborating with an interdisciplinary group that includes
Statisticians, Immunologists, Molecular Biologists, Physiologists and
Animal Breeders.
Empirical data from gene expression experiments will be available for such
studies, including oligonucleotide microarray and high throughput
quantitative PCR studies. The students' work will consist of development
and adaptation of statistical methods to answer current questions in
functional genomics and genetical genomics of livestock and wildlife
species.
Qualifications: Candidates should be highly motivated and have an interest
in molecular and statistical genetics/genomics. Ability to effectively
communicate in English is essential. A MSc degree or equivalent in
Statistics, Biostatistics, Animal Breeding or similar field is strongly
preferred. Exceptional candidates without a MSc degree, but who have
completed substantial coursework in calculus, algebra and statistics will
be considered as well. Experience with programming in R, C++ and/or
Fortran, a plus.
Tentative dissertation topics: 1) Adaptation and application of
conditionally heteroskedastic hierarchical linear models to the analysis of
gene expression data. 2) Development of computationally efficient tools
based on statistically powerful models for high-throughput analysis of gene
expression. 3) Designs and models exploiting selective phenotyping in
transcriptional profiling studies. Available projects/datasets: 1)
Differential expression of genes and pathways in response to Porcine
Reproductive and Respiratory Syndrome Virus infection. 2) Differential gene
expression in Sea Lamprey throughout its complex life history and in
response to pheromone stimulation. 3) eQTL mapping in crosses of outbred
pig populations.
Contact: Dr. Juan P. Steibel, steibelj@msu.edu