Research Fellow (fixed-term)

University of Nottingham - Veterinary Medicine & Science

Salary: £29,799 to £35,550 per annum, depending on skills and experience. Salary progression beyond this scale is subject to performance.
Interview Date: To be confirmed

This project presents an excellent opportunity to join the team of researchers working at University of Nottingham in precision livestock farming and work alongside Industry partners BT and Prognostix Ltd. 

Young-stock on cattle farms are the future for both dairy and beef industry. Both industries face challenges as optimum growth is not reached on most farms and there are young-stock losses mainly due to infectious diseases (e.g. pneumonia and diarrhoea), a lack of risk based preventive approaches, ultimately affecting welfare, productivity and reliance on antibiotics. To overcome these challenges cost-effective technologies are required, suitable for young-stock, to help improve the industry’s holistic understanding of factors effecting welfare, disease levels, and weight gain by combing sensor and non-sensor information for decision making.

Innovations in this project are targeting advanced and precision engineering, fighting antimicrobial resistance and individualised nutrition and health care for dairy and beef young-stock through (1) development of a novel cost-effective sensor for providing continuous temperature; (2) development of a communication hub that uses multiple protocols to link to multiple devices and transmit to the data-cloud; (3) Building of algorithms based on these data to provide information on health, welfare, productivity and antimicrobial usage in young-stock; (4) Development of a YWP (Young-stock Welfare and Performance) decision making platform as a decision tool for farmers, retailers and other actors in the supply chain.

The consortium comprises of specialists in engineering technology, software development, veterinary epidemiology, cloud computing and data analytics with proven track records in their subject areas and success across previous projects from which acquired knowledge and methodologies will be applied to YWare. 

You will use data fusion, feature engineering, various machine learning approaches while undertaking the research and development of analytics and algorithms for young-stock health and welfare using multi sensor data. You will work at School of Veterinary Medicine and Science and will be key member of Ruminant Population Health strategic research area of school. 

Candidates must hold a PhD, in Electrical or Mechanical Engineering, Mathematics, computer science, or other relevant field. Understanding of methods and algorithms for processing sensor data to achieve highly efficient code implementations, for data analysis, including statistical modeling and for machine learning. Strong programming skills in Python, Matlab, R or other equivalent and evidence of high quality publications in any of the listed fields. Due to multi-disciplinary nature of the project, good communication and team working skills are also essential.

Experience working with wearable technologies will be an advantage.

More details can be found at

This post is available for a Fixed-term for a period of 34 months from 1 December 2017.

Informal enquiries may be addressed to Dr Jasmeet Kaler, tel: 0115 95 16564 or email: Please note that applications sent directly to this email address will not be accepted.

It is a condition of this post that satisfactory basic disclosure is obtained.

Further details:

The University of Nottingham is an equal opportunities employer and welcomes applications from all sections of the community.

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Midlands of England