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Ecological Forecasting - summer course


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#1 Melanie Colón

Melanie Colón
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  • Texas A&M University, College Station, TX,
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Posted 08 November 2017 - 09:02 AM

July 16th-20th, 2018

Boston University

As part of the NSF-funded Near-term Ecological Forecasting Initiative this course fully funds (transportation, course, room, and board) 15 graduate students, post-docs, and early career academic scientists and 5 early career agency scientists interested in learning about ecological forecasting in a variety of contexts. This course is adapted from the newly published Ecological Forecasting book by Dr. Michael Dietze and will highlight iterative forecasting approaches.

Topics include Bayesian statistics (simple models, hierarchical Bayes, state-space models, etc); fusing multiple data sources; forecast uncertainty propagation & assessment; iterative data assimilation; machine learning; decision science; and a range of ecological forecasting applications such as phenology, microbiomes, carbon, infectious disease, and aquatic productivity.

Instructors include Michael Dietze (Boston University), Shannon LaDeau (Cary Institute of Ecosystem Studies), Kathleen Weathers (Cary), Jennifer Bhatnagar (BU), Colin Averill (BU), Barbara Han (Cary), and Melissa Kenney (University of Maryland).

Applications are due February 16th, 2018

Application materials include:

  • CV
  • Cover letter (experience with R, coding, and statistics; why you want to take this course; what sort of problems are you interested in; and why are you excited to participate)
  • Contact information for advisor (may be contacted later to provide a reference)

Application materials and any questions can be emailed to nefi.course@gmail.com.

There are no strict ‘prerequisites’ for the NEFI summer course. The course is largely R based so we will give preference to students that have a basic familiarity with R (basic data manipulation, visualization, and regression). Prior exposure to basic research computing skills (e.g. Software Carpentry)  and data management/analysis skills (e.g. Data Carpentry) is helpful but not required. Prior experience with Bayes is likewise not required.

PDF of Summer Course Flyer

Course schedule

The exact day-by-day syllabus is still being developed but is building on Prof. Dietze’s existing graduate course on Ecological Forecasting and the book that goes with the course.

We anticipate the content to be a mix of lectures and hands-on applications, including a final end-of-week group project.

 

https://ecoforecast..../summer-course/






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