Advertiser's description (via Cornell University)
Cornell's Food Systems & Global Change (FSGC) team is a part of the Department of Global Development, College of Agriculture and Life Sciences and focuses on increasing the sustainability of food systems for the benefit of humans and ecosystems. We work in the areas of sustainable intensification of agriculture, climate mitigation and adaptation, livestock systems, and healthy and sustainable diets, providing analysis and insights aimed at helping to shape the decision-making process at all relevant levels. As good decisions rely on good data, we are developing a global dataset of livestock production systems, their requirements, and impacts, and we are looking for a highly motivated postdoctoral associate that excels in programming for large datasets.
Position Function
The landmark article, Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems, has been used in global and regional studies on livestock and food systems over the last 10 years, including many Integrated Assessment Models that feed into analysis by the IPCC, IPBES, EAT-Lancet, etc. The postdoctoral associate will steer an update of this dataset by developing a global, high-resolution, spatially explicit dataset of biomass use, production, feed efficiencies, and greenhouse gas emissions of animal production systems. The work would include collecting, processing, structuring and harmonizing data, as well as developing tools to ensure the dataset is easily updatable and accessed, and ensuring good documentation. This project is a collaboration with Environmental Defense Fund (EDF) and is funded by the Cornell Atkinson Center for Sustainability’s strategic partnership with EDF. There may also be opportunities to collaborate with the FSGC team’s other related projects and to develop independently-driven projects.
Responsibilities
- Large-scale data collection, analysis, and management
- Harmonizing dataset with other global data products
- Use best-practices for reproducible coding
- Draft manuscripts and publish results in high-impact, peer-reviewed journals
- Present results at conferences and seminars
Required Qualifications
- Ph.D. degree in Data Science, Biostatistics, Bioinformatics, or a related discipline
- Experience with large datasets and data management
- Proficiency in R or Python, familiarity with modelling software like GAMS is highly valuable
- Excellent documentation skills
- Ability to work independently and as part of a diverse, interdisciplinary team.
- Strong analytic and written communication skills.
Read more here. Applications are accepted until 30 November 2022.
