Computational Agronomy

Agricultural land management is increasingly dependent on models and data from sensors, satellites and other sources, to ensure that soil, crops and livestock are managed at a very fine scale to enable precise management of nutrients and pesticides, whilst enhancing ecological processes that support agriculture.

You will seek to develop and exploit these higher quality data, that can be used to advance and apply much more sophisticated models that link process with farm management. This is an opportunity to develop a new kind of agricultural science that will integrate the current research at Leeds to improve management of agri-food systems with recent investments in “big data” analytics and at the University Farm. You will deliver high scientific, societal and commercial impact by exploiting new developments in data provision within farming and the environment and will explore how sustainable farming can be informed by detailed observations on fundamental animal, plant and environmental processes. You will also collaborate widely with biologists, farmers, engineers and data analytics experts to develop innovative forms of analysis of experiments and monitoring programmes, to inform farm management and agricultural policy from field to national scales.

You will have experience of using advanced sensing and analytical skills to investigate biological problems, have a coherent vision for the development of your research within the context of sustainable agriculture and should work closely with specialists in agricultural, plant sciences, physiological and ecological processes to be modelled with big data, as well as with specialists in IT, statistics, visualisation and bioinformatics. You will also exploit the developing network of high frequency, high resolution soil sensors at the University Farm, progressing collaborations in this area with White Rose and N8 Universities and beyond.

A proven record of achievement in international quality publications is essential to contribute to the University’s ambition to excel at REF2020. In addition, the candidate will provide research supervision and attract research students to the University and undertake a teaching load deemed appropriate by the Head of School, which will be significantly reduced in the first years of the fellowship.

University Grade 8 (£38,511 to £45,954)

For informal enquiries about the role please contact Professor Brendan Davies, tel: +44 (0)113 343 3123, email:

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