Job Description
The TCW laboratory ( in the Department of Pharmacology, Physiology & Biophysics at Boston University Chobanian & Avedisian School of Medicine in Boston, Massachusetts is looking for highly motivated and skilled computational geneticists, bioinformaticians, statisticians to be the drivers of our efforts to translate the genetics of Alzheimer's disease (AD) toward therapeutic interventions.
The core program is to better characterize the effect of the AD variants on molecular phenotypes by functional genetics and multiomics approaches. As a part of Functional Genomics Consortium (xQTL study) in Alzheimer's Disease Sequencing Project (ADSP), we are focus on methylation QTL and integrate it with other omics data (transcriptomics, proteomics, and metabolomics) to decipher the genome-wide molecular pathways associated with AD risk. In collaboration with our wet lab, we analyzed the transcriptomics and chromatin accessibility in each cell type using brain organoids from patient AD iPSCs and single cell multi-omics approach. The candidate will join a multidisciplinary and highly collaborative research team that includes computational geneticists, neuroscientists and pharmacologists in the department as well as Genome Science Institute and Center for Systems Neuroscience at Boston University.
Interested candidates should submit a cover letter outlining their research interests and career goals, a current CV, and contact information for three references to Dr. Julia TCW, Assistant professor and Director [juliatcw@bu.edu] or
Review of applications will begin immediately and continue until the position is filled.
Required Skills
Qualifications
* PhD in computational biology, bioinformatics, genetics, data science, computational neuroscience, or a related field
* Proficiency in programming languages, particularly R, bash, and Python, for data analysis and visualization
Preferred skill sets
* Experience in analyzing large-scale genomic and transcriptomic datasets
* Or, strong background in statistical methods and machine learning techniques
* Or, analysis of any of large-scale genomic datasets (RNA-Seq, ChIP-Seq, ATAC-Seq, Hi-C)
* Or, single-cell multiomic data analysis (ATAC-seq, RNA-seq, proteomics/metabolomics)
* Clear written and oral communication and detailed documentation skills, with the ability to collaborate effectively across disciplines
We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, natural or protective hairstyle, religion, sex, age, national origin, physical or mental disability, sexual orientation, gender identity, genetic information, military service, pregnancy or pregnancy-related condition, or because of marital, parental, or veteran status. We are a VEVRAA Federal Contractor.