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"Lecturer in Machine Learning for Engineering"

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Lecturer in Machine Learning for Engineering

University of Sheffield - School of Electrical and Electronic Engineering

Location:Sheffield, Hybrid
Salary:£48,822 to £58,225 (with the potential to progress to £65,509 per annum).
Hours:Full Time
Contract Type:Permanent
Placed On:3rd February 2026
Closes:31st March 2026
Job Ref:2208

The School of Electrical and Electronic Engineering seeks to appoint a Lecturer (equivalent to Assistant Professor) within the School’s Information and Communication theme and to build specialised expertise in the Machine Learning for Engineering sub-theme. Candidates from all areas in machine learning are encouraged to apply, with a special focus on the areas of (i) information theory and (ii) communications.

We seek ambitious researchers with a strong publication record and demonstrated potential to establish independent research programs of the highest calibre. Academic members will contribute to an environment of research excellence, scholarly activity, and high-quality teaching that will attract top students, world-leading researchers, and strategic industrial partners. The successful candidate will contribute to our vibrant research community and lead innovative research that addresses critical challenges in defence, and complex dynamical systems, and healthcare technologies, areas of growth for the School and aligned with the UKRI strategic priorities.

The University of Sheffield is a remarkable place to work. Our people are at the heart of everything we do. Their diverse backgrounds, abilities and beliefs make Sheffield a world-class university.

We offer a fantastic range of benefits including a highly competitive annual leave entitlement (with the ability to purchase more), a generous pensions scheme, flexible working opportunities, a commitment to your development and wellbeing, a wide range of retail discounts, and much more.

Find out more at sheffield.ac.uk/jobs/benefits and join us to become part of something special.

We build teams of people from different heritages and lifestyles from across the world, whose talent and contributions complement each other to greatest effect. We believe diversity in all its forms delivers greater impact through research, teaching and student experience.

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