To fulfill the mission of the Ronald M. Loeb Center for Alzheimer’s Disease, Drs. Alison Goate and Edoardo Marcora in the Department of Genetics and Genomic Sciences are searching for a creative, motivated postdoctoral scientist with diverse experiences and interests in genetics to join an interdisciplinary team to transform human disease genetic discoveries into testable therapeutic hypotheses.
We are looking for a statistical geneticist, genomicist, bioinformatician, genetic epidemiologist or biostatistician with extensive experience in analyzing and interpreting human genetic data and its association with complex disease and disease-relevant phenotypes (molecular or otherwise) that are relevant to disease pathogenesis (e.g., eQTLs and epigenomic annotations, clinical data, bulk and single cell transcriptomic signatures and/or cellular phenotypes from genetic or drug perturbation screens, etc).
The position will involve collaboration with dry- and wet-lab biologists to reveal mechanistic insight into disease etiology and progression, and translation to therapeutic strategies.
- Statistical design and analysis of association studies (GWAS & RVAS) from NGS and chip-based studies.
- Collaborate with computational biologist and wet-lab biologists for mechanism elucidation
- Leverage academic & industry partnerships with publicly available data for target ID and validation
- Prepare written and oral presentation of results to the team.
- Maintain and organize large amounts of genetic and genomic data and summary statistics
Qualifications and Skills
- Ph.D. in statistical genetics, genetic epidemiology, biostatistics, bioinformatics or computational biology
- Knowledge of experimental design
- Management and analysis of large genetic and genomic studies
- Knowledge of statistical genetics software
- Statistical programming skills (R)
- Programming/Scripting skills (Python/Perl/Shell/etc.)
- Knowledge of next-generation sequencing statistical analysis (sample/variant QC, association analysis)
- Knowledge of meta-analysis theory and analysis
- Knowledge of modern summary statistical analysis (LD-score regression, co-localization etc.)
- Knowledge of Mendelian Randomization
- Knowledge of single cell sequencing analyses
- Self-motivated and highly dedicated
- Excellent communication and presentation skills to both technical and non-technical audiences
- Team oriented and execution focused with an ability to thrive in a start-up environment