Access requirements

Participation to the course is competitive. The school is open to participants from governmental, non-profit and academic institutions who wish to further their skills in understanding, modeling and managing ecosystem services. Regardless of disciplinary background, each prospective candidate should possess the basic skills outlined below. All software, skills and materials obtained during the school will remain available to participants indefinitely for not-for profit use. Prospective participants from corporate and consulting businesses are welcome to apply, but should contact the school by e-mail before application to discuss potential limitations.

Necessary qualifications

The school is technical in nature and is intended for skilled, active practitioners of environmental science and modeling. The following should be intended as minimum requirements. In addition, the modeling platform used in the school is highly innovative and sophisticated. Candidates with a special interest in the fields of ecoinformatics and integrated computer modeling are encouraged to contact the school by e-mail to discuss the fit of the school to their interests.

The students must bring a recent laptop running a 64-bit operating system (Windows, Macintosh or Linux) with good disk storage and RAM (4GB minimum, more preferred). The software used in the school is not compatible with a 32-bit architecture. Please contact the school by e-mail with any questions on the adequacy of the hardware.

Skills

  • Participants should be conversant in the mainstream literature on coupled human-environmental systems and have basic experience and a good understanding of the field.
  • Basic geographic information system (GIS) skills. Familiarity with open source packages (e.g. Quantum GIS) is an added value.
  • A good understanding of the basics of environmental modeling is a prerequisite to be able to profit from the courses. Knowledge of methods such as system dynamics, integrated assessment modelling, agent-based modelling, etc. is an added value.
  • Robust mathematical and statistical skills. Familiarity with Bayesian modeling is an added value.
  • We expect most participants to be actively working in the field of ecosystem services and to be able to formulate a problem, collect basic data and evaluate results at the time of application.
  • As academic titles are often bad predictors of experience in the field, we do not impose requirements on instruction levels. However, we expect most participants to hold degrees at M.Sc. level or above.
  • Because we teach a highly interdisciplinary field, qualified applicants from diverse quantitative disciplines (environmental science, engineering, computer science, economics, policy sciences, social science) are expected and encouraged to apply.
  • The course will be held in English and full language proficiency (spoken, understood, read and written) is expected. Case study proposals that concern non-English speaking contexts are acceptable, but all teaching and assistance during the course will be conducted in English

Documents the applicants should submit

In order to apply for the school, participants need to submit their CV, a letter of intent detailing their interest in modeling and rationale for participation. Applicants should also send a 1/2-page description of how they plan to use the modeling skills taught in this course in future research or applied environmental management work (this could include a short description of a case study or general research areas of interest for applying ecosystem service modeling).The documents should accompany, the completed application form which can be accessed from here.

Upon acceptance, successful applicants may be contacted individually to define research goals more precisely to ensure a good fit with the overall school.

  1. Full CV.
  2. Letter of interest (1 page)
  3. Planned application of modeling skills taught during the course to future research or applied environmental management (could include a case study description or general description of future research interest; 1/2-page)

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