Postdoctoral Researcher


University of Pennsylvania


Philadelphia, Pennsylvania

Principal Investigator

Li-San Wang


Interested applicants should send cover letter and CV to Li-San Wang.


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 other neurodegenerative disorders and related dementia.

Our researchers apply high-throughput genotyping and sequencing technologies to analyze tens of thousands of genomes and find novel genes. New experimental approaches, algorithms, and databases are developed in order to translate these findings into biological knowledge about the disease and new directions for drug discovery and preventive strategies.

The center is highly interdisciplinary and collaborative. and includes more than a dozen faculty members who specialize in neurodegenerative disorders and dementia, human genetics, genomics, bioinformatics, and biostatistics.

Among the many scientific programs supported by PNGC are projects (ADGC, CASA, GCAD, 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 19 institutions nationwide to sequence the DNA of more than 15,000 individuals.

More information about our research program can be found at the PNGC website.


Qualified candidates should have a Ph.D. or equivalent degree in computer science, statistics, biology, epidemiology, or other related field, and demonstrate experience in programming and processing genomic data on linux environments. Expertise in genetics, algorithmic development, machine learning, data mining, and/or skills in next-generation sequencing data preferred.