This is an exciting opportunity for an ambitious graduate with the ability and confidence to manage a Knowledge Transfer Partnership (KTP) project with Fusion21 Ltd.
The University of Manchester and Fusion21 Ltd are looking to recruit an individual with evidence of a strong grounding in MLOps Software Engineering who will work alongside a Machine Learning and AI (KTP Associate) to collaboratively deliver this 24-month project. The project aims to design and deliver a disruptive technical innovation programme that embeds advanced Machine Learning, AI and software engineering techniques within the procurement process and supports the incorporation of a data-driven AI-enhanced business model and information management framework, supporting the company's strategic vision for digital transformation.
The position will provide the successful candidate with a unique opportunity to work within a multi-disciplinary team of academics, industry practitioners and another KTP associate, and translate Machine Learning, AI, and Software Engineering techniques to deliver a robust Software/AI enabled solution.
Candidates will require a PhD or master’s degree in computer science with evidence of a strong grounding in software engineering.
This post is funded through a Knowledge Transfer Partnership (KTP) award, a UK Government scheme intended to promote sustained and mutually beneficial relationships between universities and industry.
Based at Fusion21 Ltd, Unit 2 Puma Court, Kings Business Park, Knowsley, Merseyside, L34 1PJ the successful candidate will work directly with supervisors from both the University and Fusion21 Ltd and will use the facilities and resources of both organisations. Fusion21 follows a hybrid working pattern and role will offer some flexibility to work from home, but this cannot exceed 2 days in a working week and is subject to line manager approval.
Due to the nature of the funding, KTP Associates who have already completed a KTP are not eligible to apply.
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 morehere
Blended working arrangements may be considered
Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies.
Enquiries about the vacancy, shortlisting and interviews:
Name: Dr Suzanne Embury
Email:
General enquiries:
Email:
Technical support:
Jobtrain:https://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.
