Research Scientist (Machine Learning)
Location: Bellevue, WA, USA
Date Published: December 4, 2018
As part of Intellectual Ventures' Global Good initiative, the Institute for Disease Modeling’s (IDM) mission is to guide global efforts towards the eradication and control of infectious disease through the use and promotion of 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.
We seek a full-time Research Scientist to work as part of the IDM research team and collaborate on the adaptation of machine learning methods to the problem of calibrating infectious disease transmission models to pathogen genomic and epidemiological data, with immediate applications to seasonal influenza, RSV, and polio. The primary scientific responsibilities of this role include: identifying and implementing practical approaches for calibrating disease transmission models to timeseries data; working with modelers to identify model outputs required for effective calibration to data; working with software engineers to create efficient calibration tools that take advantage of parallel computing on clusters and GPUs; and visualizing complex data.
Communications responsibilities include: collaborating with external partners; presenting to key stakeholders in public health; and sharing results through conference presentations, white papers, journal publications, and software packages.
- Analysis of the literature on phylodynamic inference and machine learning on genomic data
- Develop analytical techniques to extract meaningful structure from complex genomic and timeseries datasets.
- Work with dynamical modelers to develop disease transmission models.
- Write software to automate calibration of transmission models to pathogen genomic and epidemiological data
- Communicate research through white papers, publications, conference and stakeholder presentations, and collaborations with external partners.
Key Qualifications and Required Skills:
- Ph.D. in a quantitative field (e.g. Computer Science, Statistics, Applied Mathematics, Physics, Computational Biology)
- Proficiency in current machine-learning and data science methodologies
- Proficiency in at least one data-analysis or scripting language (e.g. MATLAB, python, R)
- Multiple peer-reviewed scientific articles
- Ability to initiate, organize, and manage research projects and clearly communicate analysis results to diverse audiences
- Demonstrated ability to work productively independently and as part of a team; work extended hours to meet a deadline
Highly competitive skills:
- Experience with genomic data analysis.
- Experience with model calibration (especially approximate Bayesian computation).
- Experience with CUDA programming.
- Experience working with a software development team or on collaborative software projects.
Joining our group provides unique opportunities to interact with global-health policymakers, to collaborate with world-class research laboratories and non-profit organizations, and to contribute to global and national disease control strategies.
We are an equal opportunity employer