Machine Learning Specialist

The overall role of the job is to develop required advanced multispectral image processing and machine learning functions on a real-time embedded system for detecting cassava viral infection as early as possible from scanned leaves in the field. This role forms part of a multidisciplinary and international 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). The role contributes 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 (another post at Manchester, ref SAE-022264). The project collaborators have already been working on such applications over the past three years and several trials have been conducted in the laboratory and inhouse built devices have been upgraded, demonstrating good performances in detecting the virus. This entire international project is to expand the technology to the field, so to enable in-situ detection, characterisation and monitoring of cassava growth and subsequent quality control, funded through the NSF and BBSRC Joint Programme, collaborative EEID (Ecology and Evolution of Infectious Diseases) Programme. There are professional development and travel budgets and opportunities.

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.