As part of MSU's Global Impact Initiative, Michigan State University is seeking three outstanding researchers in statistical/quantitative/computational genomics to join the Faculty at the Assistant/Associate/Full Professor level.
The continued development of high-throughput technologies and of information systems that can store and process large volumes of health data have revolutionized human health research. These advancements create unprecedented opportunities for research and technology development and create new demands for education in areas such as quantitative genetics, statistical learning and data science. Building faculty expertise and infrastructure in computational genomics, statistical and quantitative genetics, and in precision medicine is a priority of MSU's Global Impact Initiative. The successful candidates will join a dynamic group of faculty working in quantitative, statistical and computational genomics across disciplines. Because this is part of a campus-wide MSU initiative, the open positions are not linked to specific departments. Academic department and rank will be determined based upon candidate's background and experience.Department Website Address
Applicants must have a PhD in statistics, biostatistics, computer science or related field and previous research experience in the use of genomic information for analysis and prediction of human traits and diseases, postdoctoral experience is preferred. All candidates are expected to have an excellent publication record, good communication skills and teaching experience. Senior researchers are expected to have an established research and funding record and are expected to have experience as PI of federally funded (or equivalent for non US applicants) grants.Review of Applications begins on
11-01-2016Required Applicant documents
Resume/CV; Cover Letter; Other Document; Research Interests; Learning PhilosophySpecial Instructions to Applicants
Applicants should submit a letter of interest, curriculum vitae, statement of research interests, teaching philosophy (doc referenced Learning Philosophy) and contact information for three references (applicant will be notified prior to contacting references).
Number of Reference Letters Required
No letters required