Computation Toxicology and New Approach Methodologies

What you'd do

• WHAT YOU'LL BE DOING DAY TO DAY As a Student Volunteer you will use your knowledge of and experience with Computational Toxicology and New Approach Methodologies (NAM) to optimize business results and customer experience by: Conduct comprehensive literature searches on Computational Toxicology and NAMs related to the health effects of microplastics and Nano plastics.
• Perform systematic or scoping literature reviews using established review methodologies.
• Critically evaluate and synthesize scientific evidence on computational toxicology approaches, including quantitative structure-activity relationship (QSAR) models, physiologically based pharmacokinetic (PBPK) modeling, machine learning, and in silico exposure assessment methods.
• Review and summarize the application of NAMs, including in vitro methods, high-throughput screening, organ-on-a-chip technologies, omics approaches, and adverse outcome pathway (AOP) frameworks.
• Analyze scientific literature to identify strengths, limitations, research gaps, and opportunities for integrating computational and experimental methods for risk assessment.
• Organize, manage, and document scientific references using reference management software.
• Prepare clear, concise written summaries, evidence tables, and technical reports to communicate findings.
• Identify reproducible workflows, open-source tools, and publicly available data resources that support transparent and reproducible environmental health research.
• Collaborate with CDC scientists and multidisciplinary teams to support evidence synthesis and inform environmental and public health research priorities.
• Characterizing the current state of knowledge on the physicochemical properties of microplastics and nanoplastics that influence toxicity.
• Identifying how computational and alternative methods are being applied to model their behavior, fate, and biological interactions.
• Evaluating the strengths, limitations, and data gaps associated with these approaches.
• Highlighting opportunities for integrating computational models with experimental NAM data to improve risk assessment.

What you get

• This is an unpaid volunteer internship.
• Participants are not eligible for salary, stipends, benefits, or other forms of compensation.
• Participation does not confer eligibility for federal employee benefits, including health insurance, retirement, leave accrual, or any other employment related benefits.