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Program Officer – Food security and digital advisory services
The Global Center on Adaptation (GCA) is looking for a motivated and dynamic individual to fill the role of Program Officer Food Security and Digital Advisory Services, working as a core team member to drive forward the GCA’s role as a solutions broker in the field of adaptation action.
- Helping position the GCA as a key solutions broker on climate change adaptation for achieving food security
- Taking a leadership role on climate-informed digital services, particularly in helping small-scale agricultural producers apply climate-resilient technologies and access markets.
- Working closely with partners to put in place and implement an Investment Blueprint for Climate-informed Digital Advisory Services
- Proactive assessment of relevant national, regional and international events and associated opportunities for the GCA to realise its adaptation acceleration objectives, in relation to food security and digital services.
- Develop programs to position the GCA with key stakeholders in the food security community, including maintaining active relationships with key interlocutors within these stakeholders.
- Research and prepare briefings related to identified key external stakeholders relevant to the GCA’s execution of its business plan.
- Minimum of 10 years’ experience working on issues related to agriculture and food security in low- and middle-income countries.
- Excellent experience in the analysis and/or development of climate-informed digital advisory services
- Masters’ Degree, or preferably Ph.D. in international development, agriculture, information technology or a related field.
- Excellent analytical skills, and written and oral communications skills in English;
- Excellent interpersonal skills and experience of working in international, intercultural environments;
- Excellent planning and organisational skills, enabling stakeholder knowledge to be effectively disseminated and shared within the leadership team.
- Experience in working in Africa.
Additional information about the position and the application can be found here.
PhD position – Pest and disease modelling in grain cropping systems
Understanding pest and disease dynamics within farming systems and the ability to relate incidence and seasonal conditions to grain yield loss is a clear and present challenge for the agricultural science community. Currently, management of many pests, diseases and weeds in crops relies on an estimated economic damage threshold. The threshold is used to help identify when a pest population should be reduced to prevent yield loss.
However, this approach does not consider the environment, the timing of pest control in relation to crop development, the pest lifecycle or the economic cost to future crops. Further, in many farming systems, the decision to control a pest or disease outbreak is required before the economic threshold is reached, in which case identifying and forecasting drivers for epidemics can support and improve the outcomes of pest and disease control decisions.
In contrast to traditional reactive approaches, this project will develop a proactive forecasting approach, providing end-users with the ability to plan and holistically manage pest populations well before any economic damage thresholds are attained.
The successful applicant will be awarded with a Commonwealth RTP scholarship valued at approximately $27,500 AUD/year (tax-free) for 3.5 years.
- The candidate must have an Honours Class 1 or equivalent and an IELTS score of 6.5 or greater.
- The candidate will be required to relocate to Brisbane, Australia, for the duration of the PhD.
Questions may be directed to:
Dr Matthew Harrison
Tasmanian Institute for Agriculture, University of Tasmania
Additional information on the project can be found here.
Scientific Project Officer – Crop yield forecasting with machine learning
The Food Security Unit (European Commission – Joint Research Centre, Ispra, Italy) is opening a Contract Agent position on machine learning and deep learning for yield forecasting in Africa using Earth observation and meteorological data.
The successful candidate will develop yield forecasting methods using machine learning and compare them, in terms of accuracy and timeliness, against current state of the art (mostly multiple regressions using remote sensing and meteorological data). The position is for one initial year (renewable up to six) and open for citizens of EU member states and associated countries.