This person will contribute to two NIH-funded epidemiologic studies of air pollution and dementia risk.  The first study, the Air-Noise-Dementia Study (ANDS) examines the hypothesis that long-term exposure to air pollution and noise from the community affect brain changes that we can see on magnetic resonance images (MRI) and, ultimately, the development of dementia.  The second study is entitled “Air Pollution and Alzheimer's Dementia: Neuropathologic and Olfactory Mechanisms in Multi-Ethnic Longitudinal Cohorts Dementia” (AERONoSE).  This study evaluates olfactory pathways linking air pollution exposure to dementia risk.  We will estimate the effect of air pollution exposure on neurocognitive and olfactory outcomes in 5 longitudinal cohort studies, and examine how resultant neuropathology and direct deposition of PM, assessed by advanced microscopy, explains these relationships.

The primary responsibilities of this position will be to: (1) analyze data from existing epidemiologic studies; (2) write scientific manuscripts to be submitted to and published in peer-reviewed journals; and (3) contribute to the logistics underlying the operations of these studies.

Duties will involve literature reviews; managing, cleaning, and coding data; analyzing data in SAS; interpreting and presenting data in tabular or graphical form; presenting results at scientific research conferences; drafting manuscripts for journal submission; assisting with the writing of research grant proposals; and, as needed, managing IRB protocols, coordinating and track data use agreements, scheduling web-based and in-person meetings of the investigative teams, acting as liaison to data and specimen managers at collaborating sites, developing and maintain a system for tracking progress toward the study aims.

Required Skills

• 1-3 years of experience
• Master’s degree required
• Doctorate preferred

Critical skills and experience to succeed in this position, beyond those listed previously:

• analysis of longitudinal (repeated measures) data, e.g., using linear mixed models or generalized estimating equations regression
• methods in time-to-event data
• experience in or concurrent training in methods for adjusting for selection bias and mediation (e.g., g-methods, including marginal structural models/inverse probability weighting)
• experience in or concurrent training in methods for quantitative bias analysis
• experience in or concurrent training in writing epidemiologic research manuscripts

Training or experience related to dementia and environmental health would be helpful, but are not required.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. We are a VEVRAA Federal Contractor.

Required Experience

• 1-3 years of experience
• Master’s degree required
• Doctorate preferred

Critical skills and experience to succeed in this position, beyond those listed previously:

• analysis of longitudinal (repeated measures) data, e.g., using linear mixed models or generalized estimating equations regression
• methods in time-to-event data
• experience in or concurrent training in methods for adjusting for selection bias and mediation (e.g., g-methods, including marginal structural models/inverse probability weighting)
• experience in or concurrent training in methods for quantitative bias analysis
• experience in or concurrent training in writing epidemiologic research manuscripts

Training or experience related to dementia and environmental health would be helpful, but are not required.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. We are a VEVRAA Federal Contractor.