About the Project
About the project:
This fully-funded PhD offers an exciting opportunity to undertake research on the development of a low cost, modular sensor platform for intelligent offshore wind foundation monitoring. This project is supported by a multi-disciplinary team of academics from University of Strathclyde and there will be opportunities for industry-linked collaboration with a leading offshore wind developer, Ørsted.
The goal of this project is to develop a flexible, low cost, modular platform for developing custom sensors for offshore wind foundations monitoring. The modular platform allows for rapid development for bespoke sensors to embed intelligence in offshore wind foundations.
One of the planned applications of this platform is to develop a novel sensor to de-risk the installation process for suction caisson foundations. Suction caisson foundations are increasingly used for deep water offshore wind farms (e.g. as anchors for floating wind farms or jackets in transitional waters). However, there is still much uncertainty about the installation process of these caisson foundations. To reduce the risk and uncertainty associated with the installation process, new bespoke sensors are required to capture more comprehensive and accurate information of the process. These sensor information, coupled with machine learning powered ‘autopilot’ software, will provide the caisson foundation with the intelligence and autonomy to self-install safely.
The PhD student will combine micro-electromechanical systems (MEMS) and various time-of-flight sensor techniques (e.g. ultrasonic or laser sensors) with the open-source Robot Operating System (ROS) and machine learning algorithms to produce intelligent sensing systems that provide caisson foundations with both the hardware and software to detect potential issues (e.g. soil plug lift) in real-time, and to constantly learn from data in order to make the installation process increasingly safer with more experience.
This project is suitable for a candidate who is interested in robotics, sensor development and machine learning.
The successful candidate will be based primarily at the Department of Civil and Environmental Engineering, University of Strathclyde, and will be jointly supervised by Dr Stephen Suryasentana and Dr Marcus Perry. Furthermore, the candidate will collaborate closely with industry partners such as Ørsted. The unique combination of academic and industry contacts will be highly beneficial to the candidate’s learning and career development, and future employability. There will also be opportunities for local/international collaborations and to spend a period with collaborators at University of Oxford.
This project will commence in 4 Oct 2021. The successful UK candidate will receive a fully-funded scholarship for 3.5 years, which covers all university tuition fees and an annual stipend that is in line with the UKRI guidelines i.e. £15,667 (tax-free) for the first year and at least that amount (inflation adjusted) for the subsequent years.
We would expect the candidate to have good knowledge of sensor development, sensor fusion and sensor calibration, as well as coding skills in Python. Prior experience with the Robot Operating System would be an advantage.
The successful candidate should also have (or expect to achieve) a distinction at Master’s level, or a First Class or Upper Second Class Honours degree (or the equivalent) in an Engineering or Physical Sciences subject, in particular Electronic Engineering, Mechanical Engineering or Physics.
How to apply:
Please apply by emailing your application to email@example.com by 5pm on 30 June 2021. Your application should include the following:
• An up to date curriculum vitae (CV)
• Evidence of a distinction at Master’s level, or a first class or upper second class honours degree (or the equivalent) in subjects relevant to Electronic Engineering, Mechanical Engineering or Physics.
It is recommended to apply early as interviews will be carried out on a rolling basis until the position is filled.
Informal enquires should be directed to firstname.lastname@example.org
Subject Areas include: Electrical & Electronic; Electrical Engineering; Mechanical Engineering; AI & Machine learning; Data Science; Manufacturing Engineering; Civil & Structural Engineering; Energy;
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