Biostatistician / Computational Biologist in Statistical Genetics of Complex Eye DiseasesPOSITION SUMMARY
We are seeking a highly qualified and motivated Biostatistician or Bioinformaticist to join our collaborative and interdisciplinary team in the Ocular Genomic Institute (OGI) and Department of Ophthalmology at Massachusetts Eye and Ear (MEE). MEE is a teaching hospital of Harvard Medical School, and an international leader for treatment and research in Ophthalmology and Otolaryngology. The candidate will work on innovative projects that combine human genetics, functional genomics, and statistical learning to uncover novel genetic and biological causes of common eye diseases, including diabetic retinopathy and steroid-induced ocular hypertension, and differential response to drug treatments.
The successful candidate should have a PhD in Biostatistics, Statistical Genetics, Computational Biology, Bioinformatics, Computer Science, or a related quantitative field, and strong programming skills, and should be excited to contribute to advancing the science and medicine of eye disease. Experience in large-scale data analysis, preferably genomic or next-generation sequencing (NGS), required. Research projects will include statistical analyses of genome-wide association studies (GWAS), whole genome and whole exome sequencing data, pharmacogenetic studies, and integration of genetic associations with functional genomic data, to identify novel genetic risk factors and biological processes associated with complex eye diseases and drug treatment response.
The candidate will work under the supervision of Dr. Ayellet Segr (https://www.asegrelab.org/), a statistical geneticist, and Dr. Lucia Sobrin, a clinician scientist (https://eye.hms.harvard.edu/luciasobrin) at MEE. Being a member of our groups will provide the applicant with the opportunity to contribute to clinically impactful projects and large collaborative efforts in the field of genomics and common eye diseases, and to present at local, national, and international meetings. To learn more about the OGI please visit: https://oculargenomics.meei.harvard.edu/labs/.
If interested, please send your CV, cover letter describing your previous research experience and future research interests, and contact information for 3 references to Dr. Ayellet Segr: firstname.lastname@example.org, and Dr. Sobrin: email@example.com.CHARACTERISTIC DUTIES:
- Apply and develop pipelines for preprocessing, quality control, imputation, and phasing of genotype array data, and whole exome and whole genome-sequencing data, using available and custom-built tools.
QualificationsEducation and Experience Qualifications
- Perform genetic association analyses at the variant, regulatory element, gene, and gene set levels in a large cohort to identify common and rare variants, genes, and pathways associated with steroid-induced ocular hypertension, diabetic retinopathy, and macular edema.
- Pharmacogenetic analysis of differential drug response to diabetic retinopathy and retina vein occlusion treatments using array and whole exome sequencing data.
- Build polygenic risk scores for diabetic retinopathy to help translate genetic discoveries into personalized approaches for disease diagnosis and treatment.
- Develop and apply new statistical and computational methods that integrate functional genomic and single cell transcriptomic data with genome-wide association and sequencing studies to gain biological insights into the causal mechanisms of eye disease.
- Organize all scripts in a publicly available repository (e.g., github) with clear documentation.
- Critically review, analyze, and communicate results to our team and collaborators.
- Work on both collaborative and independent projects and write up work for publications.
- Ph.D. in Statistical genetics, Biostatistics, Computational Genomics, Bioinformatics, Computer Science, Mathematics, or a related quantitative discipline required. MSc level with 3+ years of experience in genomics or omics research will be considered.
- Strong programming skills and in-depth experience with several programming languages required, e.g., Python, R, C++.
- Experience with Unix/Linux environments required, including shell scripting.
- Research experience in statistical analyses of large-scale data required; experience in statistical genetics, computational genomics, or next-generation sequencing analysis highly desired.
- Background in regression models or statistical/machine learning a plus.
- Motivation to contribute to genomic research of eye disease essential.
- Demonstrate critical thinking, rigorous work, and ability to meet deadlines.
- Strong personal skills, and excellent organization and verbal and written communication skills.
- Ability to work effectively both independently and collaboratively in a fast-paced, academic environment and evolving field.
The Segr and Sobrin labs are located in the main hospital building of Mass Eye and Ear (MEE), 243 Charles Street, in standard research and office work spaces of the Ocular Genomics Institute at MEE, with convenient access to the MEE cafeteria on the 7th floor. The candidate will work amongst a vibrant team of computational biologists, biostatisticians, research assistants, and students in the Ocular Genomics Institute, and will be part of a larger multidisciplinary research environment, which includes geneticists, experimental biologists, and clinical scientists. There will be abundant opportunities to interact with the Medical and Population Genetics community at the Broad Institute of Harvard and MIT, and with national and international collaborators. Given the ongoing COVID19 pandemic, work will be hybrid with both remote and in-person components.
Massachusetts Eye and Ear is an affirmative action/equal opportunity employer.EEO Statement
Massachusetts Eye and Ear is proud to be an equal-opportunity employer and is committed to providing a workplace free from harassment or discrimination. All employment decisions are made without regard to race, color, age, gender, gender identification, sexual orientation, religion, marital status, sex, pregnancy or conditions related to pregnancy, national origin/ancestry, citizenship, disability, military status, genetic information, or any other basis prohibited by law. These protections extend to all management practices and decisions, including recruitment and hiring practices, appraisal systems, promotions, training, and career development programs.