About the Project
Key research questions include:
- What is the relationship between problem-solving in agriculture and the development of new quantitative methods of analysis involving statistics and computing between 1920 and 1970?
- What is the relationship between agricultural research and ecology in Britain?
- What was the relationship between environmental knowledge and the ambitions of government for increasing productivity in farming in Britain and the British empire?
You will use the archives of agricultural research stations and British scientists, and relevant publications to map the contexts and networks that were important for the production of new techniques for analysing data and their relationship with ecological and agricultural knowledge in the mid-twentieth century.
You will have an interest in history (of statistics, computing, ecology or agricultural research) or ecological/environmental science methods and interdisciplinary working. There is potential for thinking about the ways in which ecologists communicate their methods (not just their findings) to a wider public audience, a key issue in building trust in science amongst society.
LCAB will provide you with opportunities to interact with other students and researchers across departments and institutions, and will offer additional training as required.
An opportunity (https://www.york.ac.uk/professorial-jobs/lcab/) is also available to submit your own proposal for a fully funded studentship with LCAB.
Students with, or expecting to gain, at least an Upper Second Class Honours degree, or equivalent, are invited to apply. The interdisciplinary nature of this research project means that we welcome applications from students with backgrounds in any relevant subject that provides the necessary skills, knowledge and experience for the project.
Shortlisting: by 22nd Jan 2021
Interviews: Feb 2021
Based on your current searches we recommend the following search filters.
Based on your current search criteria we thought you might be interested in these.
Big Data Analytics and Mining: investigating and testing distributed formulations of data mining algorithms that are suitable for the MapReduce paradigm and for other distributed computing approaches
University of Reading