Building a holistic view of molecular responses of contaminants of emerging concern using deep-learning and artificial intelligence

Research Opportunity at the United States Environmental Protection Agency

Title:

Building a holistic view of molecular responses of contaminants of emerging concern using deep-learning and artificial intelligence 

Research Area (s):

(Select one primary area and make it bold.  Select more sub areas as needed or add according to your Lab/Office/Center mission area)

• Water
• Ecosystems
• Human Health Risk Assessment
• Public Health
• Safer Chemicals

Lab, Region/Center, Office:

Center for Computational Toxicology and Exposure (CCTE), Office of Research and Development

Opportunity Location:

Research Triangle Park, NC

Opportunity Funding (Duration):

• National Science Foundation Non-Academic Research Internships for Graduate Students (INTERN) Supplemental Funding Opportunity (6 months maximum)
• Other Programs (contact research lead)

Project Description:

Not to Exceed 150 Words (Include project focus, broad goals, how does it fit in EPA/Center mission)

The project focuses on developing artificial intelligence (AI) based data integration analysis and visualization of a variety of omics data from exposure studies.  One or more of the following research topics could:  1) data collection including automatic extraction, cleaning, transformation and sorting data from public data resources, 2) self-organizing data analysis including feature extraction, clustering, classification, and generation of self-organizing map, 3) integration analysis of mixed types of environmental response data for undercovering links between different layers of biological responses  to generate a holistic view of molecular responses, 4) data visualization for result validation, interpretation and hypothesis generation.  The project supports the implementation of EPA's New Approach Methods (NAMs) Work Plan to prioritize agency efforts and resources toward activities that will reduce the use of vertebrate animal testing while continuing to protect human health and the environment.

Fellowship Goal and Learning Objectives:

Goal not to Exceed One Sentence

Goal:  The fellow will learn the use of omics data and artificial intelligence (AI) for risk assessment of environmental toxicants to the human health and ecosystem.  

Learning Objectives:

• characterize molecular responses of contaminants of emerging concern (CECs) and their adverse effects on environmental and human health.
• Learn to use cutting-edge deep learning and AI technology for data collection, analysis, and interpretation.
• Present scientific findings and write peer-reviewed journal articles.

Preferred Skills and Qualifications:

Preferred Skills:

• Strong computational and programming skills
• Solid interdisciplinary knowledge in biology, statistics, and computer science
• Ability to work both as a part of a team and independently
• Strong communication skills

Qualifications:  Graduate student or Ph.D. in one of the relevant fields (e.g., Bioinformatics, Statistics, Computer sciences, Genetics/Biology, Biochemistry).

Point of Contact or Research Lead:

Weichun Huang (weichun.huang@epa.gov)