Research Associate in Machine Learning-Based Supply Chain Analytics
Posted: 2nd September 2019
Department of Computer Science, Parks Road, Oxford.
Research Associate in Machine Learning-Based Supply Chain Analytics
Fixed-term up to 18 months
Grade 7 (£32,817 - £40,322 p.a.) (note: post may be under-filled at Grade 6 £29,176 -£34,804)
An exciting opportunity has arisen to join the department as a Research Associate/ Assistant. You will be part of a research team that applies Machine Learning techniques to identify behavioural patterns in supply chain data; build, validate and calibrate models of supply chains; use these models to predict future behaviour; assess effectiveness of these models and solutions in a real environment.
The post holder will carry out research on the “A demonstrator and reference framework IoT-based Supply Chain Digital Twin” project, in collaboration with a research group at the University of Cambridge and a major industrial company, as part of the Pitch-In project (http://pitch-in.ac.uk).
The successful applicant will possess: a doctoral degree (or be close to completion), in Computer Science, Engineering or related discipline; expertise of Machine Learning and/or AI; proven ability to write software programs and develop prototype systems for demonstration and experimental learning; knowledge of optimisation techniques; ability to manage own academic research and associated activities.
This position is offered on fixed-term contract for up to 18 months.
Whilst the role is a grade 7 position, we would be willing to consider candidates with potential but less experience who are seeking a development opportunity, for which an initial appointment would be at grade 6 (£29,176 - £34,804 p.a.) with the responsibilities adjusted accordingly (for Grade 7, you would be expected to hold a doctoral degree in Computer Science or be close to completion). This would be discussed with applicants at interview/appointment where appropriate.
The closing date for applications is 12 noon on Monday 30 September 2019. For further details and to apply please visit: https://my.corehr.com/pls/uoxrecruit/erq_jobspec_details_form.jobspec?p_id=142797
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