Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) Approaches to Analyze Alzheimer's Disease (AD) Data
Institute or Center: National Institute of Aging (NIA)
Project: Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) Approaches to Analyze Alzheimer's Disease (AD) Data
- comprehensive understanding of state-of-the-art capabilities for supervised and unsupervised ML/DL methods
- experience in applying ML and analyzing large datasets
- understanding of how to establish an open, standards-based, multi-source “plug-and-play” architecture that abrogates the need for physical device reconfiguration or user intervention to resolve system-related conflicts
About the position: The NIA seeks a DATA Scholar to launch and guide the implementation of the Cognitive Systems Analysis of Alzheimer's Disease (AD) Genetic and Phenotypic Data initiative (Cognitive Systems Analysis initiative) by applying AI, ML, and DL to unravel the architecture of the AD genome.
The Scholar will:
- Advise NIH leadership on current and future capabilities of AI to analyze massive amounts of genomic data.
- Provide recommendations on new awards under the Cognitive Systems Analysis initiative to launch the project rapidly and effectively.
- Dynamically shape goals for the initiative as data are generated and provide computational expertise to teammates.
- Optimize approaches for harmonizing and analyzing genetic and phenotypic data using AI/ML/DL approaches.
- Determine appropriate means for sharing AI/ML/DL-analyzed data.
- Provide advice on the NIA Genetics of AD Data Storage Site (NIAGADS) database infrastructure development to facilitate data harmonization, access, and analysis as AI/ML/DL outcome data become available.
- Consult with NIH leadership about linking NIAGADS with similar NIH-wide efforts for which large scale sequence data are available, enabling cross-study analysis of risk and protective factors for complex diseases such as diabetes, stroke, vascular disease, and complex mental illness.
- Engage senior leadership in broad discussions of management and analysis of large datasets.
About the work: NIA is the largest public funder of AD and related dementias research in the U.S. This component of NIA’s AD Sequencing Project (ADSP) is designed to guide the field and advance discovery of AD therapeutic targets. to support ADSP efforts. The DATA Scholar will have the opportunity to explore one of the world’s largest research databases aimed at potential treatments for AD and related dementias and other diseases.
- Clinical data include imaging, biomarker, cognitive testing, and risk factors
- NIAGADS: Datasets from multi-ethnic AD and related dementias comprised of genetics, functional genomics, clinical, epidemiological, and bioinformatics data
- ADSP: Datasets include both genomic variation and phenotype (AD features and risk factors) data
- Datasets to be harmonized by the “Harmonization of Alzheimer’s Disease and Related Dementias (AD/ADRD) Genetic, Epidemiologic, and Clinical Data to Enhance Therapeutic Target Discovery” program (see details under “Types of Data to be Harmonized”)
Why this project matters: The DATA Scholar will help open new frontiers for analysis of genetic data for numerous complex diseases and shape the direction of analyses on large, complex datasets to drive decisions on disease management.
Work location: Bethesda, MD
Work environment: The Scholar will work with NIA staff who are collaborative and highly experienced in basic and clinical research and large program management. See examples of the diverse types of research NIA supports.
To apply to this or other DATA Scholar positions, please see instructions here: datascience.nih.gov/data-scholars
This page last reviewed on January 29, 2020