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 about our benefits (opens in a new window) and join us to become part of something special.
Overview
We are seeking a Research Associate to develop new AI technologies for robotic sensing of buried pipes. You will assist in building a new autonomous robotics technology platform for buried pipe inspection. You will design, implement, test and integrate new Machine Learning algorithms for robot mapping, localisation, defects detection and classification in GPS-denied environments with small robots that will be able to navigate, communicate and sense while being led inside the pipe, under the supervision of Professor Lyudmila Mihaylova.
You will have the opportunity to work with a large trans-disciplinary team of researchers from the Universities of Sheffield, Tallinn University Of Technology (Estonia), Norwegian University of Science and Technology (NTNU), Catholic University of the Sacred Heart (Italy), UNEXMIN Ltd. (Hungary), HEROBOTS Ltd. (Italy), Headlight AI Ltd. (UK), SANDVIKA Ltd. (Norway) and other partners. x`The Grant will make use of our state of art experimental facilities at Sheffield: Laboratory for Validation and Verification (www.liv.ac.uk) and UKCRIC National Laboratory for Distributed WaterInfrastructure (https://icair.ac.uk).
Main duties and responsibilities
- 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.
- Develop a robot path planning to maximise reward for efficient coverage of the network, whilst minimising risk of the robot becoming lost and/or immobilised.
- Develop reinforcement learning strategies to see if AI methods can deliver
improved solutions. - Create sensing and machine learning methods with a variety of data such as acoustic, Lidar, optical data
- Develop methods for Simultaneous Localisation and Mapping (SLAM) with and without pre-existing maps will be developed with different levels of scalability.
- Develop solutions to data association problems, e.g. with Bayesian methods in such complex environments and validate the trustworthiness of the proposed methods.
- Develop methods for inspecting pipe networks, especially with computer vision methods (e.g. transfer learning, deep learning methods such as Yolac), detection of defects in environments of self-similar features.
- Embedding the developed methods on hardware devices as part of robot platforms.
- Collaborate with PIPEON project researchers, end-user and industrial stakeholders to develop detailed the approaches for the intelligent robots
- Disseminate project results via project meetings both nationally and internationally and via publications.
- Attend international conferences, and publish research in high-quality journals.
- Prepare project deliverables and contribute to project meetings
- Contribute to the development of further research proposals
- Any other duties, commensurate with the grade of the post.
Person Specification
Our diverse community of staff and students recognises the unique abilities, backgrounds, and beliefs of all. We foster a culture where everyone feels they belong and is respected. Even if your past experience doesn't match perfectly with this role's criteria, your contribution is valuable, and we encourage you to apply. Please ensure that you reference the application criteria in the application statement when you apply.
Essential criteria
- Hold or be close to completing a PhD in Robotics, Engineering, Physics, Mathematics (or have equivalent experience) (assessed at application)
- Experience of robotics or a related discipline such as aerospace engineering or signal processing (assessed at application and interview)
- Experience of machine learning methods such as Gaussian process regression methods, particle filters and deep learning methods (assessed at application and interview)
- Good working knowledge of relevant computer programming languages and simulation packages, for example Matlab, C/C++, Python or ROS. (assessed at application and interview)
- Strong knowledge of and demonstrable application of computer vision skills (assessed at application and interview)
- Working knowledge of optimisation methods, mapping and robot path planning (assessed at application and interview)
- Experience of publishing in peer-reviewed journals (assessed at application)
- Excellent written and verbal communication skills, including experience writing publications/reports and presenting your findings to a range of audiences. (assessed at application, interview, and presentation)
- Ability to work effectively within a team environment including individuals both internal and external to the University (assessed at application and interview)
- Ability to plan own workload and prioritise conflicting deadlines. (assessed at application and interview)
- Ability to make decisions and apply creative approaches to solving complex technical problems (assessed at application, interview, and presentation).
Further Information
Grade: 7
Duration: Fixed term until June 2028
Line manager: Professor Lyudmila Mihaylova
For informal enquiries about this job contact Professor Lyudmila Mihaylova, on l.s.mihaylova@sheffield.ac.uk
Next steps in the recruitment process
It is anticipated that the selection process will take place in February 2024. This will consist of an interview and a presentation. We plan to let candidates know if they have progressed to the selection stage in January 2024. If you need any support, equipment or adjustments to enable you to participate in any element of the recruitment process you can contact eee-postaward@sheffield.ac.uk.
Our vision and strategic plan
We are the University of Sheffield. This is our vision: sheffield.ac.uk/vision (opens in new window).
What we offer
- A minimum of 41 days annual leave including bank holiday and closure days (pro rata) with the ability to purchase more.
- Flexible working opportunities, including hybrid working for some roles.
- Generous pension scheme.
- A wide range of discounts and rewards on shopping, eating out and travel.
- A variety of staff networks, providing opportunities for social interaction, peer support and personal development (for example, Race Equality, LGBT+, Women’s and Parent’s networks).
- Recognition Awards to reward staff who go above and beyond in their role.
- A commitment to your development access to learning and mentoring schemes; integrated with our Academic Career Pathways
- A range of generous family-friendly policies
- paid time off for parenting and caring emergencies
- support for those going through the menopause
- paid time off and support for fertility treatment
- and more
More details can be found on our benefits page: sheffield.ac.uk/jobs/benefits (opens in a new window).
We are a Disability Confident Employer. If you have a disability and meet the essential criteria for this job you will be invited to take part in the next stage of the selection process.
We are a research university with a global reputation for excellence. Our ideas and expertise change the world for the better, making a real difference to society.
We know that when people come together with different views, approaches and insights it can lead to richer, more creative and innovative teaching and research and the highest levels of student experience. Our University Vision (www.sheffield.ac.uk/vision) outlines our commitment to building a diverse community of staff and students that recognizes and values the abilities, backgrounds, beliefs and ways of living for everyone.
