Heterogenous, multisource and multidimensional data mining to develop machine learning algorithms for early prediction of lameness in cattle (MED1526)

Principal supervisor: Dr Jasmeet Kaler

Other supervisors: Dr Theodore Kypraios, Prof Martin Green 

Project description:

This opportunity is based within Ruminant Population Health group at the School of Veterinary Medicine we are founder members of the national ‘Centre for Innovation Excellence in Livestock’ (CIEL) with recently opened ‘Centre for Dairy Science Innovation’. 

Lameness is one of the most important endemic diseases present in cattle around the world in terms of both animal welfare and economic loss. Currently, tools to identify lameness rely on visual subjective scoring scale and there are no predictive algorithms that can identify lameness early. Temporal data are available, collected on farms via sensors and alternative methods that will provide novel information about individual cow behaviour, genetics, claw health, milk recording, fertility etc. While recent advances in AI and machine learning techniques have boosted the potential for analysing such ‘big data’ to develop predictive algorithms, there are two key challenges: data heterogeneity in terms of type and frequency of data and feature selection and accuracy of algorithms. 

This industry linked interdisciplinary PhD project thus aims to use a range of methodologies from across disciplines of veterinary science, statistics and computer science (shrinkage, spline interpolation, machine learning especially deep learning) to overcome above challenges and create new knowledge and tools to predict lameness in dairy cows. 

The aim of this interdisciplinary PhD project is to utilise large amounts of heterogenous data collected on farms by CRV to:

The project will be based at the School of Veterinary Science with time at the School of Mathematical Science.

Further information and Application

This PhD is interdisciplinary in nature and as such would suit highly motivated applicants from a wide range of numerate, scientific backgrounds, candidates with 2.1 undergraduate degrees in mathematics or Statistics, or computer science or veterinary or animal science. MSc’s in a relevant subject such as Applied statistics, computer science, veterinary epidemiology or Data Science would be highly desirable. Experience with coding is advantageous.

Informal enquiries may be addressed to the principal supervisor Dr Jasmeet Kaler (https://www.kaler-researchgroup.co.uk/); Jasmeet.Kaler@nottingham.ac.uk

Candidates should apply online http://www.nottingham.ac.uk/pgstudy/how-to-apply/apply-online.aspx and include a CV. When completing the online application form, please ensure that you state that you are applying for a postgraduate position within the School of Veterinary Medicine and Science.

Any queries regarding the application process should be addressed to Postgraduate Admissions Officer, (email: ss-pgr-sb@nottingham.ac.uk)

Closing date: 30th June 2019

Interview Date: 

4th July 2019

Start Date: 

Sep 2019. This is a 4 year studentship funded by CRV (https://www.crv4all-international.com/). 

Closing date: 

The position will be filled when suitable candidates have been identified. Early application is strongly encouraged.

Eligibility for Funding

Only EU/UK resident 

Closing Date: 30 Jun 2019
Category: Studentships