Machine Learning Specialist

Faculty of Science and Engineering

The Faculty of Science and Engineering is one of the largest in the UK with over 10,000 students, 2,000 staff and strategic links with over 300 industrial companies. We are leading research efforts in energy, nuclear science and technology, computer science, electrical and electronic engineering, atmospheric science, bioscience and biotechnology, photon science and photonic materials, imaging and visualisation, security, and advanced materials.

Department of Electrical and Electronic Engineering

The Department of Electrical and Electronic Engineering is one of the largest and broadest of its kind in the UK and is recognised for the quality and high calibre of its research and leadership in the field.

Overall Purpose of the Job

This is an exciting opportunity for an ambitious PhD graduate or engineer with passion for applying machine learning and image processing in agriculture and farming, ability and confidence to conduct and manage a multidisciplinary, international and collaborative project between Rutgers University (USA), North Carolina State University (USA), International Institute of Tropical Agriculture (IITA) (Tanzania), Rothamsted Research (UK), and the University of Manchester (UK). You will contribute to the development of machine learning algorithms on the inhouse built low-cost, cutting-edge potable multispectral imaging systems for detecting cassava brown streak virus in the field. The project partners have been working on such application over the past three years and several trials have been conducted in the laboratory and the inhouse built devices have been upgraded, demonstrating good performances in detecting the virus. This project forms a pivotal part of the on-going effort to expand the technology to the field, so to enable in-situ detection, characterisation and monitoring of cassava growth and subsequent quality control. The overall role of the job is to develop required advanced multispectral image processing and machine learning functions on acquired data (leaf scans from field and laboratory) and on a real-time embedded system for detecting cassava viral infection as early as possible.

You will work for The University of Manchester but may be required to travel to collaborative partners in USA and Tanzania for conducting experiments, with supervision at the University and regular meetings with the entire project team.

What you will get in return:

• Fantastic market leading Pension scheme
• Excellent employee health and wellbeing services including an Employee Assistance Programme
• Exceptional starting annual leave entitlement, plus bank holidays
• Additional paid closure over the Christmas period
• Local and national discounts at a range of major retailers

As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status.  All appointments are made on merit.

Our University is positive about flexible working – you can find out more here

Hybrid working arrangements may be considered.      

Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies.

Any recruitment enquiries from recruitment agencies should be directed to People.Recruitment@manchester.ac.uk. Any CV’s submitted by a recruitment agency will be considered a gift.

Enquiries about the vacancy, shortlisting and interviews:

Name: Professor Hujun Yin

Email: Hujun.yin@manchester.ac.uk

General enquiries:

Email: People.recruitment@manchester.ac.uk

Technical support: jobseekersupport.jobtrain.co.uk/support/home

This vacancy will close for applications at midnight on the closing date.

Please see the link below for the Further Particulars document which contains the person specification criteria.

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