University of Washington School of Public Health
Researcher or Postdoctoral Fellow, Model Based Geostatistics (NTD) - Institute for Health Metrics and Evaluation
Closing Date: open until filled
Posted: April 19, 2018
The Institute for Health Metrics and Evaluation (IHME) is an independent research center at the University of Washington focused on expanding the quantitative evidence base for health. IHME has an outstanding opportunity for a Researcher whose work will focus on Neglected Tropical Diseases (NTDs) on the Geospatial Analysis team. The purpose of this position is to apply innovative methods in geospatial analysis to produce high-quality and policy-relevant estimates of health and health-related indicators at the most granular level possible, with a focus on lymphatic filariasis, human African trypanosomiasis, and onchocerciasis.
The Researcher will be a critical member of an agile and dynamic research team developing new approaches and producing detailed estimates that will empower policymakers and donors to make optimal decisions about allocating funds and prioritizing interventions. The individual will be expected to interact successfully and describe complex concepts and materials concisely to a wide range of stakeholders, including high-level individuals in government or other organizations.
Through the development and use of geospatial techniques to synthesize information at the local level, and in partnership with key collaborators around the world, IHME will present results in interactive high-resolution maps to illuminate levels, trends, and disparities in health outcomes.
The Researcher must develop a command of the methods developed and deployed and the rationale for them. In this case, model-based geostatistics are used to model the spatial variation in prevalence of key diseases and model geographical variation in NTD prevalence and burden. The individual is expected to agilely deploy code to carry out complex statistical methods. The individual will be a key contributor to discussions about methods development, strategic decision-making about how best to deploy methods using the given computational infrastructure, and most important, how to improve upon the results given the available data.
The individual will work with data from vital registration, surveys, administrative sources, and scientific literature, and will need to become familiar with global and national patterns in the epidemiology. The Researcher will assess all available data and use established modeling tools to produce results. He/she will write papers and contribute to methodological advancements over time. The individual will also be responsible for contributing to papers, presentations, and other materials to help disseminate results.
The individual will work with senior research leads and faculty and take part in the intellectual exchange about how to improve upon the results and in creating papers and presentations that help share the results with broader audiences. The individual will be expected to interact successfully with a wide range of stakeholders and to describe complex concepts and materials concisely. Overall, the Researcher will be a critical member of an agile, dynamic research team. This position is contingent on project funding availability.
Research command and analyses
- Develop a core understanding of the geospatial methodology and its components.
- Carry out quantitative analyses and participate in collaborative research projects.
- Develop, critique, and improve estimates of neglected tropical diseases using geospatial methods.
- Identify, review, and assess data sources in order to determine their relevance and utility for ongoing analyses. These may include vital registration data, hospital data, and data from the scientific literature. Become expert in understanding key data sources and, in particular, variations in these across and within countries.
- Transform and incorporate data, choosing and applying methods, identifying areas for estimation improvement, and conveying results to diverse audiences.
- Develop and implement new computational and statistical methods. Create, test, and use relevant computer code (Stata, R, or Python). Maintain and distribute completed software, as needed.
- Communicate with external collaborators in order to best understand the nature, key characteristics, and context of the data and engage in critiques of the analytic results.
- Become expert in understanding the demographic, geographic, and social characteristics in a country that might generate disparities or be useful for population comparisons.
- Develop and maintain relationships with designated collaborators. Respond to and, as appropriate, integrate feedback from collaborators into the analyses. Work directly with collaborators to understand data to which they have access, and to in turn help them understand the methods being applied. Help to manage and orchestrate joint strategies for analysis.
- Effectively communicate and work with other staff at all levels in order to achieve team goals for the analyses and related outputs.
- Contribute and develop ideas for new research projects.
Publication and dissemination
- Write and lead publication of research findings in international peer-reviewed journals and other publications.
- Present papers at national and international conferences to disseminate research findings.
- Represent the research group at external meetings, seminars, and conferences.
- Lead discussion in research meetings about results and analyses in order to vet, improve, and finalize results.
- Document code and analytic approaches systematically so that analyses can be replicated by other team members.
- Support project leaders in the development of new funding proposals.
- Become a fully contributing member to the IHME team overall, lending help and support where needed, participating in mutual intellectual critique and development with colleagues, and acting as a mentor to more junior staff contributing to the research process.
Master’s in public health, epidemiology, statistics, biostatistics, or related field plus three years’ related experience, or equivalent combination of education and experience.
- Disease- and/or risk-specific expertise, including familiarity with data sources and epidemiology.
- Demonstrated interest in the research described.
- Experience of and demonstrated success in modeling using at least one of the following programming languages: Stata, Python, R.
- Excellent analytical and quantitative skills.
- Ability to undertake research projects with limited guidance.
- Excellent communication skills, including track record of success in writing for publication, presenting research proposals and results, and representing research groups at meetings.
- Ability to thrive in a fast-paced, team-oriented research environment with a focus on producing innovative, policy-relevant results.
- A theoretical and practical understanding of disease modeling.
- PhD in public health, epidemiology, statistics, biostatistics, or related field.
- Experience in geostatistical modeling of diseases.
Conditions of employment:
Evening and weekend work may be required.