Research Associate in AI & Machine Learning Methods for Robots Inspecting Underground Sewer Systems

We are seeking a Research Associate within the School of Electrical and Electronic Engineering to work on the PIPEON: Robotics and AI for Sewer Pipe Inspection and Maintenance.

You must have a PhD (or be close to completion, or with relevant, equivalent experience) in Robotics, AI areas, electrical engineering, mathematics, or a related area. The following background will be useful: machine learning, statistical signal processing, probability theory, Bayesian inference optimisation methods or equivalent experience, deep learning, computer vision, sensor data fusion methods as well as experience of developing software in Python/ MATLAB/ ROS. You will also be experienced in conducting research projects both individually and as part of a team.

You will contribute towards the creation of resilient, uncertainty-aware methods for resource-constrained robot path planning, creation of topological and semantic maps for robots to operate with scene perception under limited battery resources. Creativity is essential for developing different methods, including Bayesian methods (e.g. transfer learning), deep learning methods (e.g. Yolact) and embedding them on hardware devices as part of robot platforms. These include methods for classification and segmentation of the defects, in the process of scene perception that the sewer pipe domain imposes.

The School of Electrical and Electronic Engineering (EEE) belongs to the prestigious Faculty of Engineering, which is one of the largest academic communities of engineers in the UK, ranked joint 1st in the UK for Research Environment in the last Research Excellence Framework (REF 2021). Our specific discipline (EEE) is also ranked very highly, which is a marker of our excellence and ambition (QS 2024: 11th in UK, The Guardian 2024: 3rd, The Times Higher 2024: 7th in the UK).

We are home to several national institutes and centres (e.g. National Epitaxy Facility, The National 6G Radio Systems Facility, Rolls-Royce Control, Monitoring and Systems Engineering University Technology Centre) and we strive to conduct fundamental research and innovation to address global challenges, as well as support the education of the next generation of engineers.

What We Offer:

• 41 days annual leave (including closure days and Bank Holidays), with the possibility to purchase up to ten additional days.
• Up to 5 days of paid time off to cover emergency caring responsibilities.
• A highly competitive pension scheme.
• Discounts at participating shops and cinemas, and a cycle to work scheme.
• A commitment to your development, with programmes, learning modules and mentoring schemes.
• An inclusive and diverse workplace.
• A commitment to your wellbeing, with confidential emotional support, support for those going through the menopause, experiencing fertility treatment and having a baby, paid leave for IVF treatment and more.

Further information can be found at https://www.sheffield.ac.uk/jobs/benefits.

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 www.sheffield.ac.uk/benefits and join us to become part of something special.

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