Consultants- Adaptation Insights Project at Alliance Bioversity & CIAT

Main responsibilities

• Attend a two-week training to get familiarized with the evidence synthesis research topic and protocol, data extraction tool, and quality control processes.
  
• Screen publications against predefined inclusion and exclusion criteria, using human-assisted machine learning methods.
  
• Extract relevant data on adaptation effectiveness following a structured protocol, including causal evidence of effectiveness, study design, data collection methods, ensuring comprehensive coverage of gray and peer-reviewed literature.
  
• Ensure data quality and consistency, verifying data accuracy, validity, and reliability.
  
• Contribute to the refinement of the data extraction system by providing feedback on workflow improvements and reporting technical issues.
  
• Participate in regular team meetings and report progress via internal communication tools (e.g., Slack, Google Sheets, Notion).
  
• Contribute to data analysis and co-author research outputs, based on interest.

Applicants are invited to visit https://www.bioversityinternational.org/jobs/ to get full details of the position and to submit their applications. Applications MUST include reference number Ref: (RFP300315) – Consultants- Adaptation Insights Project: as the position applied for. Applicants should submit the following documents:

• A two-page CV clearly highlighting relevant experience and knowledge with evidence synthesis methods and climate adaptation projects. Include the names and contact details of three referees familiar with your work.
  
• A cover letter containing the following information: motivation for the consultancy; explanation of how your qualifications align with the required competencies; and answers to the following questions: (i) What are key variables (5-10) you would extract in a systematic review on the effectiveness of agricultural adaptation strategies? Briefly justify your choices (one short sentence per variable); (ii) When extracting data on causal evidence of adaptation effectiveness, what key criteria would you use to distinguish strong from weak causal claims in the literature? Answer these questions in your own words. We will be using AI detection tools to verify the originality of the submitted content

Application deadline: 7th March, 2025