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Senior Research Associate - Aerospace Digitalisation

Loughborough University - Wolfson School of Mechanical Electrical and Manufacturing Engineering

Location: Loughborough University, Loughborough, Hybrid/On-site
Salary: Specialist and Supporting Academic Research grade 7 from £45,585
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 28th February 2024
Closes: 18th March 2024
Job Ref: REQ240134
 

Full time, fixed term until 31 March 2026

Job Grade: Specialist and Supporting Academic Grade 7

We are seeking a highly experienced person to lead research and development into next generation digitalization methods involving digital-twin architectures (and possibly AI/machine learning) and their application to complex systems typically found in aerospace applications.

Digital-twins have emerged from more traditional engineering modelling and simulation activities. However, the key difference is that the digital-twin interacts with its physical counterpart in a way that ensures the digital-twin mimics the behaviour and response as observed by the physical counterpart. Consequently, the digital-twin can be executed in non-real-time, for example running in slower or faster than real-time so critical analysis can explore unexpected or unanticipated behaviour that has yet to appear in the physical counterpart.

The proposed research will adopt a multidisciplinary approach using digital-twins to investigate methods to couple multiple digital-twins as part of a co-simulation environment. The concept here is that for complex systems several digital-twins will be required, spanning different levels of fidelity from surrogate to high fidelity models. Understanding how these different digital-twins will communicate with each other and with certain aspects of the physical counterpart is of significant research interest. Domain specific tacit knowledge (the inherent skills experts have accumulated over many years) is regarded as a key component that ideally needs to be embedded into the digital-twins. In evolving the digital-twin models we are interested in how tacit knowledge could be captured and embedded into the digital-twin.

The research is an integral part of an exciting research project led by Professor Roy Kalawsky and is co-funded by the Royal Academy of Engineering and Airbus. The research will be undertaken as part of a multidisciplinary team with Airbus.

Additionally, the role will involve co-supervision of other researchers and doctoral students whose research is aligned to other Airbus related research. The research is likely to lead to important new intellectual property which needs to be carefully protected. Although, there will be excellent opportunities to generate high quality scientific reports and papers suitable for publication in International Journals. This is a unique opportunity to contribute to a Nationally important project that will improve the way commercial aircraft are designed in the future.

Key Requirements  

  • Significant relevant experience within a high-quality research or development environment.
  • Significant relevant experience of computer programming with a preferred focus on modelling, simulation and possibly AI/machine learning.
  • Have significant experience of taking a leading role in a research lab (either at university or in industry), with proven experience of management projects/major work packages and resources (e.g. budget, equipment, or other people).
  • Evidence of advanced technical knowledge, including first authorship of peer-reviewed paper(s)/presentations at internal conferences.
  • Evidence of having communicated their research to external industrial partners/major funding bodies
  • Representation of research centre’s activities and research outcomes at international conferences or events. 
  • Evidence of managed other researchers  

For more information refer to the Job Description and Person Specification. 

Informal Enquiries

Informal enquiries are welcomed and should be directed to Professor Roy S. Kalawsky by email r.s.kalawsky@lboro.ac.uk 

Application closing date: 18 March 2024

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