Lecturer in Machine Learning for Engineering

• Posting date: 02 February 2026
• Hours: Full time
• Closing date: 04 March 2026
• Location: Sheffield, S10 2TN
• Company: University of Sheffield
• Job type: Permanent
• Job reference: 2208-43943622

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, 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 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.

We invite applications from candidates who can contribute to the School\'s research and teaching activities in machine learning for engineering, with emphasis on information theory and communications as noted above.