Post-doc in statistical genetics – Roslin Institute, University of Edinburgh
4 Febreiro 2015Convocatoria dos Premios de Investigación Ernesto Viéitez 2015
9 Febreiro 2015Post-doc in statistical genetics – Roslin Institute, University of Edinburgh
4 Febreiro 2015Convocatoria dos Premios de Investigación Ernesto Viéitez 2015
9 Febreiro 2015The CASCADE project at Lawrence Berkeley National Laboratory (LBNL) is looking for a postdoctoral researcher to develop and apply statistical methods to the study of extreme weather events in a changing climate.
The postdoc will work with statisticians at the University of California, Berkeley and climate scientists from LBNL as part of the interdisciplinary Calibrated and Systematic Characterization, Attribution, and Detection of Extremes (CASCADE) project.
We seek a statistician with expertise and interest in statistical methods relevant for climate/atmospheric/environmental science. The position offers an excellent environment for working with a highly skilled interdisciplinary team in the Climate Sciences Department and Computational Research Division at LBNL and the Statistics Department at UC Berkeley. The expertises of team members include Bayesian and spatial statistics, climate analysis, climate change detection and attribution, climate modeling and dynamical systems, and high-performance computing. The successful candidate will focus on analysis of a variety of types of extremes including droughts, downpours, heat waves, atmospheric rivers, tropical cyclones, and hurricanes. Understanding such events is an area of intensive current research in the climate science community and of interest to the public at large.
The goal of this position is to develop and use statistical methods to detect and characterize extremes with an emphasis on quantifying the changing risk of these phenomena from anthropogenic influences. The position entails using a combination of statistical methods such as spatial and spatio-temporal statistics, extreme value analysis, the bootstrap, and Bayesian methods to estimate the probabilities of climate events under different scenarios. A key focus will be to quantify the uncertainty in the probabilities in light of a wide variety of sources of uncertainty, including sampling uncertainty and model error. The researcher will evaluate, extend and implement existing methods and develop new statistical frameworks and methods.
The researcher will work with climate scientists to apply the methods to cutting-edge datasets of observations and model output, including models and data products developed and run at LBNL.
More details and application information available at https://lbl.taleo.net/careersection/2/jobdetail.ftl?lang=en&job