美国韦恩州立大学分子医学和遗传学中心博士后职位

Two post-doctoral positions are available in the research groups led by Francesca Luca and Roger Pique-Regi, at the Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI (http://lucalab.wayne.edu). The research focus of the groups is on the genetic and molecular characterization of gene regulation. Examples of collaborative projects include: characterizing variation in the response to hormonal and environmental stimuli, identifying tissue-specific cis-regulatory modules with DNase-I footprinting, and detecting signals of selection and adaptation in gene regulatory regions. The ultimate aim is to learn about the genetic basis of disease susceptibility and response to treatment.

We have a strong record in using both functional and evolutionary genomics approaches. We use a combination of high throughput experimental platforms and computational/statistical tools. We seek applicants who are very creative, energetic, and can work independently. We operate relatively small but well-funded and intense laboratories. The goal is that everyone should have the resources, support and mentorship needed to be successful and become an independent investigator.

The two positions are intended for applicants with complementary expertise. Specifically we are looking for talented individuals with either a strong experimental and/or computational background that will contribute complementary expertise to create a team jointly supervised by Dr. Luca and Dr. Pique-Regi.

The experimentalist position would be under the direct supervision of Dr. Luca, and requires experience in collecting genome-scale data, in mammalian cell and tissue culture techniques and in molecular genetics techniques. Familiarity with the quantitative skills required for the analysis of genomic data (e.g., Python, R, scripts for setting up an analysis pipeline) is also required.

For the computational position (under the direct supervision of Dr. Pique-Regi), applicants should have a strong background in quantitative/statistical skills, and a very strong interest in biological applications. A background in genomics, gene regulation or statistical genetics is an advantage, although we will consider outstanding candidates with quantitative degrees (e.g. in Statistics, CS, or Engineering)