Academic Supervisor: Dr Julian Bass
Academic Co-Supervisor: Dr Tarek Gaber
Industrial Supervisor: Hossein Ghavimi
The studentship is fully funded and includes:
- An MPhil/PhD fee waiver
- A starting stipend of £18,000 p.a. for three and a half years
- All bench fees and consumable costs
- Funds specifically allocated for conference travel
Final date for applications: 31/05/2021
Interviews will be held on: 15/6/2021
The candidate must be in a position to register by 27/9/2021
Description:
The aim of this research project is to use video data and the latest machine learning techniques to detect component deterioration in Internet of Things applications. We are excited by the opportunities presented by the latest Deep Active Learning approaches which you will have a chance to enhance and apply in this research. On the one hand, active learning (AL) maximizes a AI model’s performance gain using as few samples as possible. On the other hand, deep learning requires large quantities of training data to extract high-quality features. Deep active learning techniques are exciting because they seek to combine the strengths of both approaches. The Internet of Things and Industry 4.0 are exciting application areas for AI researchers, with considerable growth in recent years.
While you will have a chance to develop your own research plan, we envisage these aims will be achieved through three main phases of work. First, establishing a laboratory-based experimental apparatus for testing and evaluating approaches to video detection of asset degradation. Second, creating novel approaches to video detection of asset degradation. Thirdly, using video data to overlay identification information derived from databases.
This is a great opportunity to join an award-winning research team and to work on a cutting-edge research project applying AI in an Internet of Things application. The successful candidate will become a thought leader in artificial intelligence and have the opportunity to work with potential clients and company subject matter experts.
Computer Science at Salford is ranked in the top 400 departments world-wide (Times Higher Education, World University Ranking, 2021) and Julian Bass was University of Salford, Research Supervisor of the year in 2020 and is Head of the Informatics Research Group.
Add Latent Ltd. and the University of Salford have been collaborating for over five years. This includes two award winning Knowledge Transfer Partnership (KTP) projects which both received top-scoring certificates of excellence from Innovate UK. Add Energy were also recently awarded the Queens Award for Enterprise in International Trade due to their fast pace of growth.
Add Latent Ltd. have already created an R&D capability using an agile software development process and have an impressive client list of major companies in the industrial marine and energy sectors. Their expertise in maintenance optimisation gives them unique access to large industrial plants.
Candidates:
The successful candidate will join the Informatics Research Group at University of Salford and develop expertise machine learning and video processing. In addition, the Add Latent team will be on-hand to advise about asset integrity,
You will hold (or expect to obtain) a Masters degree or minimum of an upper second class honours degree in an area of computer science, software engineering or a closely related discipline. Experience of working in the commercial digital technology sector would be an advantage.
As part of your application please provide a CV and covering letter.
For full details of student requirements and specification please visit: http://www.salford.ac.uk/ktp/industrial-case-studentships/vacancies
Funding Eligibility:
This studentship is only available to students with settled status in the UK.
Enquiries: Informal enquiries may be made to Julian Bass by email: [Email Address Removed]
Curriculum vitae and supporting statement explaining their interest should be sent to [Email Address Removed]
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