Job Description

Job Description

This is for a 6-month full-time co-op from July to December 2021

Summary
Our group, global biometrics non-clinical and clinical biomarker biostatistics, consists of biostatisticians, machine learning researchers, and statistical geneticists. We develop innovative statistical pipelines for upcoming technologies such scRNAseq, snRNAseq, and spatial transcriptomics to aid in the discovery of new drug targets. Our team members also create statistical solutions for problems in research, biomarker development, and PO&T in order to increase Biogens probability of success.

Position Description
Spatial transcriptomics is a new technology to perform mRNA expression profiling of a tissue while also providing spatial information of the gene expression activity. This spatial gene expression information can yield new insights into disease pathology, gene expression in distinct biological compartments, expression profiles in rare cell populations, and expression profiling along contours. Specifically, Biogen is interested in spatial transcriptomics to understand lesion development and expansion in multiple sclerosis (MS).
Analytics and Data Sciences in conjunction with Translational Biology will co-lead the internship project. The intern will focus on improving Biogens current analysis pipeline for spatial gene expression profiling by reading associated literature, developing exploratory simulations to compare analysis approaches, and finally implementing the analysis pipeline as an R package. If time permits, the intern will work on integration of single nuclei RNAseq (snRNAseq) and spatial transcriptomics data for mouse models as well as human brain MS lesions.
Specifically, the intern will:
Investigate literature and available R/python packages to discover the latest algorithms for spatial gene expression analysis
Perform simulation and benchmarking studies to determine performance characteristics of different spatial analysis approaches
Refine/improve current statistical analysis pipelines at Biogen for spatial gene expression analysis. This may involve theoretical exploration of different spatial correlation structures, model forms, missing data imputation strategies, QC filtering approaches, or visualization algorithms
Develop an R package which implements a standardized analysis pipeline for spatial gene expression analysis and outputs results in a specific data format

Qualifications

Qualifications
To participate in the Biogen Internship Program, students must meet the following eligibility criteria:
Legal authorization to work in the U.S.
Grade point average of 3.2 or higher preferred
At least 18 years of age prior to the scheduled start date
Be currently enrolled in an accredited college or university

Additional Qualifications:
Experience with R and/or python programming
Experience with biostatistics and biostatistical simulation
Some familiarity with gene expression technologies (microarray, RNAseq, scRNAseq, snRNAseq, etc)

Education
MS or PhD in biostatistics or related area

Location
Cambridge, MA

Additional Information

All your information will be kept confidential according to EEO guidelines.