Epidemic Modeling of Real-time Generation and Spread of Substance use & associated health harms
Postdoctoral Researcher in Modeling/Prediction of Drug Use Related Epidemics
Applications are invited for a postdoctoral fellowship in modeling of drug use and associated health harms in the Division of Infectious Diseases and Global Public Health (IDGPH) at the University of California San Diego (https://medschool.ucsd.edu/som/medicine/divisions/idgph/Pages/default.aspx).
Over half a million people died of drug use-related harms across the world in 2019, including from overdose, HIV and HCV. This number is far greater when accounting for alcohol-related deaths and suicides, which often share the same root causes. Socio-economic and health inequities, limited behavioral health services, as well as other social and structural issues, contribute to an increased susceptibility to alcohol, drug use and mental health disorders. Our research group in the IDGPH division works on a range of projects to characterize and predict these epidemics and to inform their public health response using statistical and dynamic modeling in various settings with a focus on the United States, Mexico and Australia.
We are looking for candidates with a statistical/machine learning or/and a dynamic modeling (i.e. deterministic compartmental or agent-based modeling) background, with experience working in the field of public health and with a strong interest in improving the health of marginalized populations and of people who use drugs in particular.
Researchers with dynamic modeling skills will lead the development of overdose, HIV and HCV transmission models among people who use drugs and apply these models to estimate the contribution of specific determinants (such as changes in the drug supply) to these epidemics and the potential impact of different intervention strategies in multiple settings. They will leverage data from local cohort studies and from the published literature for parameterization and calibration.
Researchers with a statistical/machine learning background will focus on the development of predictive models of fatal overdose as well as multiple outcome prediction models of fatal overdose, suicide and alcohol related deaths in the United States over the short term (i.e. one year or less). They will also work on the prediction of drug use disorders in the long term (i.e. 10 years). This will require the curation of databases bringing together data from multiple public and restricted data sources.
The postdoctoral scholar will work under the primary supervision of Dr. Annick Borquez (https://profiles.ucsd.edu/annick.borquez). Applicants should have a PhD in a relevant discipline (applied mathematics, epidemiology, engineering, statistics and related fields) and be proficient in Matlab, R or C++. Scholars will benefit from biweekly seminars and trainee sessions on substance use, HIV and related infections, journal clubs, and NIH grant writing circles. The IDGPH division is highly interdisciplinary and offers opportunities for collaboration with epidemiologists, clinicians, statisticians, policymakers, legal experts and public health researchers.
The initial appointment will be for one year, with option for renewal for an additional year based on performance. Salary will be commensurate with qualification and experience and include travel to conferences and funding for relevant courses. Strong support to apply for independent NIH funding (e.g. K01) will also be provided. Candidates from under-represented minority populations are encouraged to apply. Remote positions within the U.S. will be considered.
Review of applications will begin immediately and continue until the position is filled. Applicants should submit a cover letter, their CV, and at least two names of potential referees. For further information, please contact Annick Borquez (firstname.lastname@example.org).