Position Description:
We are seeking 2 outstanding post-doctoral research fellow candidates with doctoral expertise in one of these specialties: biostatistics, epidemiology, statistical genetics, bioinformatics, neurobiology, computational biology or a related field and with strong communication and writing skills, to fill 2 postdoctoral research fellowship positions at the Glenn Biggs Institute of Alzheimer’s & Neurodegenerative Diseases.
The selected candidate will have the opportunity to analyze large-scale phenotypic, genetic and multi-omics data in a collaborative research setting of international studies on dementia and cerebrovascular disease to identify novel biology and druggable targets underlying Alzheimer disease, vascular cognitive impairment, and resilience in cognitively normal super-agers.
Under the supervision of the Director of the Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, Dr. Sudha Seshadri, the selected candidate will join a highly productive team of senior and junior faculty, post-doctoral fellows and graduate students to identify her or his own core areas of interest, lead publications, work with a multi-dimensional team, submit and win training grants and be ready to transition to faculty positions, typically in 2-3 years.
The selected candidate will work with rich phenotypic, imaging, genomic and multiomic data available through the Framingham Heart Study, the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, the Trans-Omics for Precision Medicine (TOPMed) program, the Alzheimer’s Disease Sequencing Project (ADSP), the International Genomics of Alzheimer’s Project (IGAP). The co-mentoring team will include Biggs faculty such as Dr. Claudia Satizabal (epidemiologist), Dr. Bernard Fongang (bioinformatician), Dr. Xueqiu Jian (genetic epidemiologist), Dr. Habil Zare (computational biologist) and others at University of Texas Health Sciences Center San Antonio, University of Texas San Antonio, University of Texas Rio Grande Valley and Boston University as most appropriate for the specific areas of research being developed. ,
Prior experience with large-scale biological datasets, multi-omics integration, or machine learning is advantageous but not essential for an otherwise strong candidate. The candidate should have excellent computational, organizational, communication, and problem-solving skills, and have the ability to work both independently and as part of a multi-disciplinary team.
Please send an email with your letter of motivation and CV.