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are seeking a PhD candidate to join our young team (Computational Systems Medicine) within the context of the BMFTR-funded DIASyM (https://diasym.mscoresys.de/ ) project, to develop new statistical and
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initiatives including the International Cancer Benchmarking Partnership (ICBP). Opportunities for Career Development This position offers an outstanding opportunity for an ambitious epidemiologist or health
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selection, to clinical trials at HCI. We interact with other data scientists, bioinformaticians, statisticians, epidemiologists, psychometricians, health economists, survey design researchers, and systematic
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selection, to clinical trials at HCI. We interact with other data scientists, bioinformaticians, statisticians, epidemiologists, psychometricians, health economists, survey design researchers, and systematic
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modelers, epidemiologists, and economists. Development, calibrate, and extend of microsimulation and compartmental models of tobacco use and lung cancer in R. Receive training in modeling methods
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). Demonstrated leadership to supervise other statistical team members in the healthcare setting including other statisticians, system reporting specialist, and population health epidemiologists. Prior leadership
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unit within the Kansas Center for Rural Health. In collaboration with a multidisciplinary team of public health scientists, demographers, sociologists, and epidemiologists, the project manager will help
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environmental public health assessments (details at https://www.atsdr.cdc.gov/pha-guidance/index.html). In collaboration with a multidisciplinary team of toxicologists, environmental health scientists, and
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allow you to collaborate with a broad group of researchers through the Climate, Environment and Health programme (https://sph.nus.edu.sg/research/climate-environment-and-health-programme) . You can find
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
team including epidemiologists, biostatisticians, and clinicians. Goals of the project are to develop new approaches to handle mismeasured endpoints in causal inference and to apply the new approaches