(Fully-funded) PhD Studentship – IoT-based Inferential Measurement for Asset Integrity

(Fully-funded) PhD Studentship

IoT-based Inferential Measurement for Asset Integrity

Robert Gordon University

Closing Date: 7 January 2021

Applications are sought for a (fully funded) Research Studentship (PhD) to carry out research at Robert Gordon University, Aberdeen, United Kingdom, under the supervision of Dr Andrei Petrovski and Dr Stewart Massie, with co-supervision from their research teams (Cyber Security and Intelligent Systems)

Duration and Funding

The duration of project will be up to 36 months, ideally commencing 1 February 2021 or as circumstances allow. The studentship covers both tuition fees (at Home / EU or International level) and a tax-free stipend of £15,000 per annum.

Proposed Research

Inferential measurement is a powerful and increasingly used methodology that can enhance the monitoring and maintenance of various complex systems, which form part of companies’ assets (e.g. drilling rig, production plant, or smart building), through estimating difficult to measure characteristics by acquiring sensor data. The information obtained through inferential measurements can be used to assure the integrity and to optimise the operation of such assets with the help of IoT technologies capable of gathering massive amount of data and of processing these data in an intelligent way.

To make this data processing efficient and effective, several optimisation problems need to be addressed, including connectivity, coverage, routing, and data transmission. These problems can be solved using techniques from evolutionary computing, constraint-satisfaction, data mining and fusion. In particular, the usage of edge computing in order to achieve localisation, parallelisation and distribution of information processing looks promising in developing inferential measurement systems.

The proposed project has the following objectives:

  • to develop inferential components (known as soft/virtual/smart sensors) that use computational intelligence (CI) and machine learning (ML) techniques for carrying out condition monitoring and diagnostic tasks;
  • to build an inferential measurement system based on these components for fault detection and analysis of asset integrity;
  • to deploy these intelligent components on IoT platforms;
  • to integrate such platforms into asset monitoring frameworks, such as digital twins.

Key Skills

Applicants should have a very good BSc (Honours) (First or Upper Second class) degree or a Master degree (with Distinction or Merit) in Computing Science, Biostatistics or related discipline.

Essential Knowledge and Experience:

  • Strong programming skill in Python/C#/Matlab or similar languages.
  • Experience with development and testing environment(s) of IoT technologies, such as IoT emulators and physical kits (e.g. Arduino, STMicroelectrinics, Pycom boards or the like).
  • Good analytical skills – knowledge of foundations of computer science and networking; an ability to think independently.
  • Strong oral and written communication skills, in both plain English and academic languages, for publication in relevant journals and presentation at conferences.

Desirable requirements

  • Experience or knowledge in Computational Intelligence and Machine Learning.
  • Knowledge of simulation (Matlab, Simulink, etc.) and machine learning packages (Tensorflow/Keras, NLTK or similar).
  • Mixed-method evaluation including knowledge of statistical tools (R/Minitab or similar).

Applicants should have good personal and communication skills, strong professionalism and integrity, and be capable of working on their own initiative.

Enquiries

Enquiries can be emailed to Kate Lines at soc-researchadmin@rgu.ac.uk and will be forwarded to Dr Andrei Petrovski if technical in nature.

Applications

Applications should be emailed to Kate Lines at soc-researchadmin@rgu.ac.uk by 7 January 2021. The applications should consist of a covering letter or personal statement of interest, and a CV. Further information such as passport details or transcripts may be requested during the short-listing stage. Interviews (which may include a short practical test) are expected to take place week commencing 19 January 2021.