PhD Studentship- Development of machine learning algorithms for early prediction of lameness in cattle using heterogenous and ir University of Nottingham, Department of Veterinary Medicine & Science United Kingdom

PhD Studentship- Development of machine learning algorithms for early prediction of lameness in cattle using heterogenous and irregular data

Reference
MED1516


Closing Date
Friday, 22nd February 2019


Department
Veterinary Medicine & Science

 

Principal supervisor: Dr Jasmeet Kaler

Other supervisors: Prof Martin Green, Dr Theodore Kypraios

 

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 (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:

  • Develop and compare algorithms using supervised, unsupervised and semi-supervised machine learning methods that can predict claw health problems in cattle 

  • Validate the developed algorithms in the field by collecting new data 

 

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

 

Further information and Application

 

This PhD is interdisciplinary in nature and as such would suit applicants from a wide range of numerate, scientific backgrounds, including (but not limited to) candidates with 2.1 undergraduate degrees in Veterinary Science or Animal Science or Statistics, or computer science. MSc’s in a relevant subject such as Applied statistics, computer science, veterinary epidemiology or Data Science would be an advantage. 

 

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: 22nd Feb 2019

 

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

 

Interview Date: 

1st of March 2019

 

Start Date: 

15th March 2019 or as soon as possible thereafter. This is a 4 year studentship funded by CRV (https://www.crv4all-international.com/). 

 

Eligibility for Funding

Only EU/UK resident 


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