Canon Foundation for Scientific Research (CFSR) sponsors scientists primarily in the scientific and engineering disciplines in developing countries. The foundation aims are to support activities that will increase the participation of under represented nations in these disciplines and to develop an understanding of how this will influence development.

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Target group: Master’s degree, PhD degree
Purpose: Study, Research
Field of study: Agriculture, forestry and fishery , Engineering, Environmental Science, Medical and health sciences, Natural sciences
Countries: All
Eligible candidates: high-quality female researcher. Note that application has to be done by the National Commission for UNESCO of applicants country of origin.
Value and purpose of grant: $40.000
Grant award period: two years

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This Fellowship (a bequest from Helen S. Bentley and C. Fred Bentley) provides assistance to Canadian and developing-country graduate students with a university degree in agriculture, forestry or biology, who wish to undertake postgraduate, applied, on-farm research with cooperating farmers in a developing country.

In principle, IDRC supports research on all parts of the developing regions of the world. At this time, Fellowships and Awards is not supporting awards which involve research in Burma, Cuba, Iran, Iraq, Eastern Europe or Central Asia.

Projects should evaluate and/or promote the use of fertility enhancing plants, such as leguminous forages, shrubs, cover crops, and grain legumes in small farms. The intent is to seek ways to increase the yield of food crops, improve farmers’ livelihoods, and improve soil fertility.

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CU-SIM: Modeling and Analysis Skills for Sustainable Infrastructure Restoration

DOCTORAL STUDENT CANDIDATES

The Civil Engineering Department at Clemson University invites applications for twoGraduate Assistantships for Ph.D. Students to start August 2010. The Graduate Assistants will investigate the coupling of Single Phenomena Models into one Multi-Phenomena Model, and the application of statistical methods to quantify uncertainty, determine parameter sensitivity and infer parameter values from experiments.

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