PhD Studentship – Distributed and Intelligent Sensing for Ocean Research and Sustainable Ocean Enterprise

This EPSRC PhD Case Studentship research aims to develop hardware & software systems to improve industrial, scientific and environmental processes in the sea. This research will co-design, operate and assess new distributed sensing platforms to enable step-changes in the acquisition and interpretation of marine data. The data- provisioning network comprises sensor hardware, communicating sub-systems, data processing and integration with simulations and control systems.

The PhD, in partnership with Sonardyne, will focus on: new systems to enable underwater network capabilities; measuring the right parameters and transforming unwieldy measurement data volumes into useful information; new “smart” modes of operation of sensors (training operation on existing multi-year datasets) to increase data availability and/or value and, interfaces to allow a combined simulation-measurement approach.

Industrial Collaboration: Sonardyne are a global supplier of subsea technology and instrument solutions, whose core capabilities include use of acoustic, optical and inertial technologies for accurate navigation, positioning, communications, monitoring and imaging. An existing collaboration between the School of Engineering and Sonardyne is nucleating some of the fundamental components that this deeper research will take to the next level. Access to Sonardyne know-how, hardware, and aligned R&D will be provided. Over the course of the PhD, the student will spend three months working at Sonardyne to implement, integrate, and test the research outputs in‑situ.

This project is funded by a 4 year EPSRC Industrial CASE PhD Studentship, with enhanced stipend. Candidates should be eligible for an EPSRC award, should hold (or expect) a first degree (min grade 2.1) in Physics, Engineering, Informatics, Computer Science or other relevant discipline and should have solid background in one or more of the following topics: fluid dynamics, statistics, signal processing, communication networks, and oceanography.

Assistant Supervisor: Dr. Thomas Culverhouse (Sonardyne)