Assistant Professor (Research) – Data and AI Analytics for Manufacturing Applications (111399-0426)

Position Overview

The Warwick Manufacturing Group seeks a researcher to support its data‑driven AI strategy in UK manufacturing innovation.

Responsibilities

• Define requirements for real and synthetic data collection aligned with project priorities, and develop best practice guidelines.
• Develop exploitation models, including licensing, IPR, and hosting data as a tangible product.
• Facilitate cross‑cutting activities between research groups, IT, and business development to identify gaps and opportunities.

Qualifications

• PhD in Engineering, Computer Science or a related field (e.g. data science).
• Experience and a sustained track record in research methodologies for real and synthetic data collection.
• Experience delivering data‑driven impact, through AI training or other exploitation routes.
• Ability to work as part of a dynamic research team and collaborate across multiple research groups.

Required Skills & Experience

• Proven ability to engage with diverse research groups and translate data research challenges into actionable plans.
• Strong communication skills and capacity to work in an interdisciplinary environment.

Working Conditions

• Position based at the University of Warwick.
• Proof of right to work in the UK is required; sponsorship may be considered on a case‑by‑case basis.

Benefits

• Attractive pension scheme.
• 30 days holiday plus University Christmas closure.
• Generous parental/adoption leave policy.
• Onsite childcare facilities.
• Excellent learning and development opportunities.
• Flexible working arrangements.

Equality, Diversity & Inclusion

The University of Warwick is an equal opportunity employer and welcomes applications from all backgrounds. We actively encourage candidates from under‑represented groups.

Disability and Ex‑Offender Rehabilitation

• We will not discriminate against applicants who make a disclosure related to a conviction.
• Established procedures to remove bias in the shortlisting process.

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