Research Intern - Data, Dynamics & Analytics
Location: Bellevue, WA, USA
Date Published: March 13, 2020
The Institute for Disease Modeling (IDM), part of Intellectual Ventures’ Global Good program, is committed to improving and saving lives in developing countries using quantitative analysis. The IDM team is composed of research scientists and software professionals who create advanced models of disease transmission, develop computational tools to inform global disease eradication policy, conduct analysis of epidemiologically- and policy-relevant data, and identify critical knowledge gaps. IDM is a highly dynamic organization with a work environment that is defined by innovation and collaboration. As part of our work, we routinely collaborate with groups at the World Health Organization, the Center for Disease Control, PATH, the Bill and Melinda Gates Foundation, ministries of health in the developing world, as well as universities and research institutes.
The Data, Dynamics, and Analytics group within the Institute for Disease Modeling has an internship opportunity for a graduate student pursuing a Ph.D. in a quantitative field. Over the summer of 2020, the intern will be applying cutting-edge, data-driven techniques for analyzing time-series data collected from modern epidemiological surveillance systems. We are seeking a candidate passionate about learning and applying new mathematical and statistical techniques as well as taking a deep dive on real data. For example, the candidate will be using methods such as the recently developed sparse identification of nonlinear dynamics (SINDy) and dynamic mode decomposition (DMD) on time-series data collected in low-and-middle income countries focused on pathogens such as polio, measles, flu, and diarrheal diseases. More broadly, this internship offers an opportunity to learn about various quantitative approaches in epidemiology, to understand current public health questions and challenges, and to engage with a cohort of smart, enthusiastic interns focused on a variety of questions.
- Read academic literature on recent equation-free methodologies.
- Implement these algorithms from scratch.
- Apply these methods to real epidemiological data.
- Analyze and interpret the results of the method and the overall performance.
- Document incremental progress on internal facing wiki pages.
- Regularly present progress to internal IDM team members.
- Familiar with classic time-series analyses.
- Ability to read and write academic papers.
- Basic knowledge of computational statistics (e.g. Monte Carlo).
- Proficiency in python, matlab, or R programming is required.
- Works well in a team setting.
- GitHub experience a plus.
- Attention to detail and high standard for quality of work.
- Demonstrated ability to work productively independently and as part of a team.
We are an equal opportunity employer