Deadline to Apply: Monday, September 5, 2016
The Poulsen Lab at Duke University (http://poulsenlabduke.com(link is external)) is seeking an outstanding postdoc to study tropical forest ecology in Central Africa. The research will consist of two major foci: (1) Evaluation of how changes in the animal community cascade to affect forest structure, composition and ecosystem services through the modification of ecological processes; (2) Quantification of tropical forest structure in relation to environmental and disturbance gradients. Specific topics include, but will not be limited to: seed and establishment limitation, forest dynamics and carbon stocks, and plant-animal interactions.
The position will consist of overseeing existing research projects in Central Africa, writing funding proposals, and analyzing data and writing scientific publications. Approximately one-quarter of the position will involve overseeing research projects in French-speaking Africa. These will include finishing an ongoing experiment on the effects of plant enemies on tree recruitment. One-half of the position will consist of contributing to the analysis and writing of high impact manuscripts from existing datasets/projects. One-quarter of the position will entail creating and implementing novel research related to the above topics.
As such, we are looking for candidates with good field skills, strong quantitative, programming, and modeling skills, and a demonstrated ability to write and publish. A basic knowledge of French is helpful, but not necessary.
The appointment is for 1.5-2 years, to begin as soon as possible.
Minimum Qualifications Required:
- Candidates must have obtained their PhD in ecology, environmental science, applied statistics or a related field by the appointment start date.
- Statistical and programming skills in R are essential.
- Demonstrated writing skills, as evidenced by a record of publication, are necessary.
- Candidates must demonstrate the ability to work well independently and as a part of a team.
- Experience with Bayesian analysis and fitting of maximum likelihood models is preferred.
- Familiarity with GIS is beneficial.
- The best-qualified applicants will also have exceptional reasoning and analytical skills, and experience working or ability to work as part of an interdisciplinary team of scientists.
Interested applicants should apply through AcademicJobsOnline: https://academicjobsonline.org/ajo(link is external) (See POSTDOC #7377).