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Position: Research Assistant in Environmental Epidemiology
Institution: London School of Hygiene & Tropical Medicine
Location: London, United Kingdom
Duties: The Environment & Health Modelling (EHM) Lab is looking for a highly motivated research assistant to conduct environmental epidemiology analyses using novel epidemiological methods. The role will focus on analyses for the Constrained estimation project, although the successful candidate will also have the opportunity to be involved in other projects from the EHM Lab members. The EHM Lab is a team of researchers based in the PHES department at LSHTM. We have multi-disciplinary expertise spanning biostatistics, environmental epidemiology, data science, statistical computing and climatology
Requirements: Candidates are required to either have an undergraduate degree in statistics or epidemiology, or have strong experience with advanced statistical such as linear and nonlinear regression modelling as well as time series analysis. Required skills include a good knowledge of the R software and as well as epidemiology designs and analysis. The successful candidate should be willing to familiarise themselves with the state-of-the-art methods in environmental epidemiology and engage in rigorous scientific practice
   
Text: Research Assistant in Environmental Epidemiology The Environment & Health Modelling (EHM) Lab is looking for a highly motivated research assistant to conduct environmental epidemiology analyses using novel epidemiological methods. The role will focus on analyses for the Constrained estimation project, although the successful candidate will also have the opportunity to be involved in other projects from the EHM Lab members. The EHM Lab is a team of researchers based in the PHES department at LSHTM. We have multi-disciplinary expertise spanning biostatistics, environmental epidemiology, data science, statistical computing and climatology Candidates are required to either have an undergraduate degree in statistics or epidemiology, or have strong experience with advanced statistical such as linear and nonlinear regression modelling as well as time series analysis. Required skills include a good knowledge of the R software and as well as epidemiology designs and analysis. The successful candidate should be willing to familiarise themselves with the state-of-the-art methods in environmental epidemiology and engage in rigorous scientific practice
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