Multiple Postdoctoral Opportunities at Berkeley
Analysis and simulation of infectious disease surveillance system datasets in California and China
Multiple postdoctoral fellows are sought for positions working with Justin Remais at the UC Berkeley School of Public Health, who is leading several new studies (NIH-NIAID R01AI125842, 2017-22; UCOP MRPI, 2017-2021; NSF 1646708, 2016-19; and the continuing NIH-FIC R01TW010286, 2015-20) with U.S. and international collaborators, focused on the analysis and simulation of infectious disease surveillance data in California, China and elsewhere. The newly-funded NIAID R01 is establishing a Berkeley-led, international research consortium that will develop approaches for simulating and optimizing surveillance networks to detect existing and emerging infectious diseases under changing epidemiological and environmental conditions. The research team will develop and apply spatio-temporal data integration techniques for assessing the performance of specific surveillance architectures, and a simulation platform for optimizing surveillance system performance under alternative configurations and constraints. The project will apply these tools to ‘Big Data’ from multiple surveillance data streams in China—in collaboration with U.S. CDC and China CDC partners—in order to identify the timing, geographic scope, and type of surveillance that maximize detection of tuberculosis, malaria, schistosomiasis, leptospirosis, dengue, hookworm and infectious diarrhea. Other new and ongoing projects in the group focus on the transmission dynamics of infectious diseases in changing environments, focused on the industrialization of agriculture in West Africa (R01TW010286), drought in California (UCOP MRPI), and climate change in Ecuador and China (NSF 1646708).
Postdoctoral scholars will have opportunities to contribute to the extension of inventory study methodology—i.e., capture-recapture epidemiological designs—into a longitudinal framework capable of characterizing the dynamical response of surveillance systems to changing epidemiological conditions; develop simulation platforms for integrating surveillance system data, and for running experiments to identify optimal configurations under a range of scenarios; and lead the development of mathematical modeling techniques for investigating the response of disease transmission to a range of exogenous perturbations. Scholars will work closely with collaborators on these projects who are leaders in their fields, including Joe Eisenberg at University of Michigan; Howard Chang, Ben Lopman and Lance Waller at Emory; Alan Hubbard at Berkeley; and Manoj Gambhir at Monash.
Applicants should have a PhD and a demonstrated record of scientific achievement in statistics, biostatistics, data science, infectious disease epidemiology, population biology, theoretical ecology, or similar quantitative biological field, and should be proficient at programming (e.g., R, Python, Matlab or similar). Experience with Hadoop, AWS, Spark, cloud computing, spatio-temporal modeling, and/or modeling dynamical systems would be highly desirable. Candidates with backgrounds in mathematics or applied mathematics, computer science, engineering, the quantitative environmental sciences, or physics are additionally encouraged to apply. A track record of research excellence and strong quantitative skills are essential, as are excellent written and oral communication skills.
Interested applicants should submit a curriculum vitae, a 1-2 page letter that describes in detail the professional qualifications for the above-described activities, and contact information for three referees, to Justin Remaisjvr@berkeley.edu.