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Cynwyd, Pennsylvania 19004, United States of America [map ] Subject Areas: Industrial Engineering Physics Statistics Mathematics Economics (more...) Management Science & Engineering Computer Science and
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Cynwyd, Pennsylvania 19004, United States of America [map ] Subject Areas: Industrial Engineering Physics Statistics Mathematics Economics (more...) Management Science & Engineering Computer Science and
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, Pennsylvania 19004, United States of America [map ] Subject Areas: Physics Industrial Engineering Statistics Mathematics Economics (more...) Management Science & Engineering Computer Science and Electrical
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ecology, and/or restoration ecology. Experience in design, execution and analysis of acoustic data is desired. Knowledge on statistical methods and their application is an extra merit. Good knowledge in GIS
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(or equivalent) in biology, bioinformatics, genetics, molecular ecology, or a related field. Solid knowledge and practical experience in molecular ecology, population genetics, and statistical and bioinformatic
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species and vegetation ecology), advanced statistical modelling using R software, and conducting fieldwork under harsh environmental conditions. A successful applicant should have good skills in English and
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record of scientific productivity is preferable. The successful applicant must have a Ph.D.in Computer Science, Data Science, Statistics, Mathematics, Physics, Engineering or related field at the start of
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knowledge in plant species identification. The successful candidate should be able to independently conduct statistical analyses in R and hold a valid driver's license. Experience in GIS and handling large
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datasets, therefore, there will be a focus in the implementation of models for large volumes of data. The project will work in an exciting interface of statistics and machine learning and has the potential
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demonstrable experience in academic writing for publication, e.g. peer reviewed paper(s) and/or report(s) Advanced statistical software skills are desirable, e.g., regression analyses, repeated-measures analysis