Postdoctoral Researcher in Computational Genomics

University of Pennsylvania
Penn Neurodegeneration Genomics Center
United States PA Philadelphia


Penn Neurodegeneration Genomics Center (PNGC) is seeking applications for postdoctoral researchers interested in the development and applications of algorithms, statistical models, and big genomic data mining and machine learning methods in human genetics and genomics, with focus on Alzheimer's disease and related dementia (ADRD). Example projects include:

" Analysis of GWAS and whole genomes to find novel genetic variants associated with ADRD;
" Translating genetic associations to identify perturbed genes, cellular context, and downstream pathways;
" Analysis of deep phenotypes with genetic data for gene-exposure interactions, health economic analysis, and disease risk modeling;
" Novel algorithms and resources for big genomic data analysis using machine learning, data mining, and artificial intelligence on cloud computing environments.

The Center is highly interdisciplinary and collaborative, and includes more than a dozen faculty members who specialize in neurodegenerative disorders and dementia, bioinformatics, biostatistics, human genetics, and genomics. Among the many scientific programs supported by PNGC are projects (ADGC, CASA, CGAD, NIAGADS) funded by National Institute on Aging (NIA) to build new cohorts, coordinate analysis, and disseminate data and findings for Alzheimer's disease. These projects form the national hub for AD genetics research and drive the AD Sequencing Project (ADSP), a key NIA initiative with ~150 scientists from 20 institutions nationwide to sequence the DNA of more than 30,000 individuals.

More information can be found at the PNGC website:


A Ph.D. or equivalent degree in computer science, statistics, genetic epidemiology, biology, or other related fields required. Experience in handling genomic data, expertise in genetics, algorithms, machine learning, data mining, and/or skills in next-generation sequencing data preferred.

Start date

As soon as possible

How to Apply

Interested applicants should send cover letter and CV to Michelle Moon (


Li-San Wang