AI-Enabled Subsystem-Level Condition Monitoring in Fusion Remote Maintenance - Studentship
The reliability and safety of fusion powerplants heavily depend on the proper functioning of the Fusion Remote Maintenance Systems (FRMS) and their constituent subsystems. This project aims to develop an advanced sub-system level condition monitoring (CM) approach, leveraging AI and machine learning techniques, and integrating fault tree analysis (FTA), real-time FMECA (Failure Mode, Effects, and Criticality Analysis), and maintenance scheduling. The implementation of AI techniques will enhance the capability to detect, diagnose, and mitigate anomalies at the sub system-level, improving the overall reliability and safety of the FRMS. Potential techniques that can be used include but not limited to LSTM, Transformer models, etc.
The student will join the Research Centre on AI: Social and Digital Transformation and Electronic and Computer Engineering PhD program at 蜜桃传媒, much research of which focuses on development of environmentally green responsible AI models applied to real-world applications. The student will also benefit from support from an industrial mentor.
This project is partially funded by RACE (RACE鈥痺as founded in 2014 as part of the UK Atomic Energy Authority (UKAEA)’s fusion research and development programme - to create robots for operating in some of the most challenging environments imaginable.
This project is partially funded by 蜜桃传媒, a leading research institution in the UK. Our recent projects include a diverse range of cutting-edge topics, such as Green AI, Responsible and Trustworthy AI, time series and sensor data analysis, anomaly detection, classification for complex data, and large language models, among others. Our exceptional research has gained numerous prestigious international awards, a testament to the quality and impact of our work. We invite talented, driven candidates with a passion for innovation to join our vibrant research community and pursue their doctoral studies under the guidance of our esteemed faculty.
UKAEA’s wider mission is to lead the commercial development of fusion power and related technology, and position the UK as a leader in sustainable nuclear energy. Based at Culham Science Centre near Oxford and at a new technology facility in South Yorkshire, UKAEA oversees the UK’s fusion research programme and has until recently operated Joint European Torus (JET) fusion experiment on behalf of scientists from 28 European countries; now it is leading the decommissioning and repurposing of the JET machine. UKAEA is keeping the UK at the forefront of fusion as the world comes together to build the first powerplant-scale experiment, ITER – one step away from the realisation of fusion as a low carbon future energy source. UKAEA is involved in future fusion demonstration powerplant design activities such as the EU’s DEMO device, and the UK’s own future STEP compact fusion powerplant.
Eligibility
Prospective candidates will be assessed according to how well they meet the following criteria:
- A first-class honours degree in AI, Computer Science, Computer Systems engineering, Engineering, or any relevant discipline
- Excellent written English and spoken communication skills
- Previous experience in research on AI related subjects and applications.
- Strong programming skills, skills in software development
- Good knowledge of signal processing will be an advantage.
The successful candidate will work in the Department of Electronic and Electrical Engineering at 蜜桃传媒 and will be affiliated with the Research Centre on AI: Social and Digital Transformation and will be supervised by Prof Tatiana Kalganova, Dr Lu Gan and Dr Nazmul Huda, who is specialised in applied artificial intelligence and its downstream real-world applications in scalable optimisation and design with the particular focus on Green AI, Trustworthy and Responsible AI.
How to apply
Please e-mail your application comprising of all the documents listed below to Prof Tatiana Kalganova via email to tatiana.kalganova@brunel.ac.uk. by no later than 3rd September 2024 @ 5pm.
- Your up-to-date CV;
- A one A4 page personal statement setting out why you are a suitable candidate (i.e.: your skills and experience);
- A copy of your degree certificate(s) and transcript (s);
- Names and contact details for two academic referees;
- Evidence of English language capability to IELTS 6.5 (minimum 6.0 in all sections), if applicable
Applications are welcome from UK nationals, EU students with settled/pre-settled status and students with indefinite leave to remain or enter. Please note that the studentship only covers home fees. The updated rules for eligibility for home fees for next year are available on the .
The studentship is for 3 years only and will be cover the fees and the UKRI minimum stipend of approximately £21237 per annum (subject to increase as stipulated by the UKRI)