We are seeking a research assistant (RA) to help develop “real” landscapes for agent-based models of bees as part of an EU Horizon 2020 research project. The successful applicant will be based in the research group of Professor Jane Stout in the School of Natural Sciences, Trinity College Dublin, will work closely Professor Chris Topping and his team in the Department of Bioscience, Aarhus University, and with collaborators in Teagasc, and will join the dynamic and interdisciplinary PoshBee[1] team.
The RA is required to:
- Collate information on crop growth and management in Ireland
- Code this information into the ALMaSS framework[2] using a python script
Key skills required
Essential:
- Programming skills, using Python programming language
- Understanding of Irish agricultural system and farm management
- Good communication abilities to actively engage data providers, as well as the geographically-distributed team.
- Structured approach to project planning and execution
- Languages skills – must be fluent in English.
Desirable:
- GIS skills, experience with R
Salary: This appointment will be made at point 1 of the RA scale from the Irish Universities Association Researcher Salary Scales i.e. €23,061 per annum (gross) for 6 months from 1st December 2020.
To apply: please send letter of application, outlining suitability for the post, and a CV, to Prof. Stout stoutj@tcd.ie by Monday 23rd November 2020.
Project description
Pollinators face multiple threats including agrochemicals, pathogens, habitat loss and climate change. A major project PoshBee (Pan-European Assessment, Monitoring and Mitigation of Stressors on the Health of Bees) aims to understand the impacts of these multiple pressures on a range of bee species and develop novel tools to help reduce risks and negative impacts. Our findings will help to ensure that pesticides can be used safely while protecting wildlife, health and the environment, both in Ireland and internationally. The RA will contribute to a workpackage on systems and agent-based modelling approaches to assess the synergistic effects of multiple stressors on bee health.
[1] This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 773921
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