Postdoc - Data-driven Approaches for Improved Crop Protection

Postdoc - Data-driven Approaches for Improved Crop Protection

SLU - Swedish University of Agricultural Sciences

Uppsala, Sweden

About the position

Understanding the factors that drive crop pest and pathogen outbreaks is central to the economic and environmental sustainability of agriculture. Our understanding of how climatic conditions and crop management affect pests is, however, still incomplete, hindering the development of decision support systems for more effective crop protection. The postdoc will analyze large digitized monitoring datasets collated over decades in tens of thousands fields to assess how crop pests and diseases are affected by current climatic conditions. and predict changes under future climates. Based on the results, the postdoc will carry out in-depth and fine-tuned analyses for selected key crop pests and pathogens, including the role of crop management practices (e.g. crop rotation), to concretely support crop advisors and farmers with pest management decisions.

The postdoc will coordinate data collation and analysis, as well as write and publish scientific papers in collaboration with the research team. The postdoc will be advised by Associate Professor Ola Lundin with expertise in entomology and ecology, Associate Professor Hanna Friberg with expertise in plant pathology and Professor Giulia Vico with expertise in modelling plant-environment interactions. The postdoc is also expected to contribute to team activities, assist in student supervision and communicate with stakeholders.

Your profile

We are looking for highly motivated candidates who are actively pursuing an academic career. You should have documented research experience and an interest in the research area. The position requires a Ph.D. in biology, ecology, agricultural engineering, environmental science, agronomy or an equivalent subject. Strong documented expertise in statistical analysis is required. Experience in compiling and analyzing large datasets using mixed models, machine learning or other statistical approaches suitable for large datasets is meriting. You should have a documented ability to independently initiate, conduct, and complete research projects and to generate and publish the findings in international peer-reviewed journals. Written and oral communication skills in English at an academic level are required. The candidate is expected to be able and willing to take initiative and to work both independently and in a team.

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