PhD Studentship in Machine Learning for Stress and Fatigue Detection in Ship Crews
University of Southampton School of Engineering
United Kingdom

PhD Studentship in Machine Learning for Stress and Fatigue Detection in Ship Crews

School of Engineering
Location: Highfield Campus
Closing Date: Monday 31 August 2020
Reference: 1248220DA
Supervisor: Dominic Hudson, Dominic Taunton, Sarvapali Ramchurn

Project description

It is often reported that 80% of maritime accidents are due to human error, with a large proportion of these attributed to a failure to follow procedures. The usual response to this is to increase training. A recent research project (MARTHA) which looked into fatigue and sleepiness onboard ships, concluded that although all crew showed increased levels of fatigue by the end of a voyage, certain crew member were more susceptible to tiredness and that fatigue and stress were inter-related.

The aim of this project will be to measure and predict the levels of fatigue and stress in targeted Deck Officers from modern LNG ships. This will be carried out using a range of both intrusive and non-intrusive methods. This will be combined with vessel and motions data and environmental data. Machine learning techniques (e.g., deep learning and Bayesian classifiers) can be used to determine periods of acute stress and poor sleep leading to fatigue. Based on these outputs, optimisation algorithms will be developed to reduce tiredness and stress levels. Moreover, this information can be used to develop training programmes incorporating appropriate stressors to reduce the negative effects of stress/ fatigue as the trainees become habituated. The project will also investigate the levels of activity and stress when off watch and when not at sea, in particular the periods before and after a long sea voyage.

The ideal candidate will have a background in Machine learning, Ubiquitous Computing, Artificial Intelligence, and Human Factors or Human-Computer Interaction. Candidates with a background in Engineering or Psychology will be considered. Depending on the background of the successful PhD student, suitable training will be provided from specialist modules across Engineering and Health Sciences. The student will be given the opportunity to learn about ship operation and crew training.

The recent Global Marine Technology Trends 2030 report highlights increasing technology onboard ships and the need for highly skilled crew to operate them. The need to recruit highly skilled crew for ship operations will require significant development of training. The design of shipboard systems needs a multi-disciplinary approach building on work already carried out between engineering and psychology.

Working with our industrial partner, Shell Shipping and Maritime, this research has the potential to reduce major shipping accidents, saving lives and reducing environmental impact.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: applications should be received no later than 31 August 2020 for standard admissions, but later applications may be considered depending on the funds remaining in place.

Funding: full tuition fees for EU/UK students plus for UK students, an enhanced stipend of £15,285 tax-free per annum for up to 3.5 years.

How To Apply

Applications should be made online, please select the academic session 2020-21 “PhD Eng & Env (Full time)” as the programme. Please enter Dominic Hudson under the proposed supervisor.

Applications should include:

Curriculum Vitae

Two reference letters

Degree Transcripts to date

Apply online:

For further information please contact: feps-pgr-apply@soton.ac.uk


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