Overview
This editorial highlights a PhD opportunity centered on utilizing machine learning to assess stress and fatigue levels among maritime crew members. Given that a significant percentage of maritime accidents stem from human error, this research aims to enhance safety protocols through innovative technological solutions.
Background & Relevance
The maritime industry faces considerable challenges related to human error, often exacerbated by fatigue and stress among crew members. Previous studies, including the MARTHA project, have indicated that fatigue levels increase significantly during voyages, particularly among certain crew roles. Understanding and mitigating these factors is crucial for improving safety and operational efficiency in maritime contexts. The integration of machine learning techniques into this domain presents a promising avenue for developing effective monitoring and intervention strategies.
Key Details
- Application Deadline: 31 August 2019 (standard admissions), with potential for later applications based on funding availability.
- Funding: Full tuition coverage and a tax-free stipend of £15,009 per annum for UK students, available for up to 3.5 years.
- Research Focus: Measurement and prediction of fatigue and stress in Deck Officers using both intrusive and non-intrusive methods.
- Methods: Utilization of fitness trackers, environmental sensors, vessel tracking data, and machine learning algorithms.
- Training Opportunities: Candidates will receive training in relevant software and modules across Engineering and Health Sciences.
Eligibility & Participation
This PhD position is open to candidates with a strong academic background in fields such as Machine Learning, Ubiquitous Computing, Artificial Intelligence, and Human Factors or Human-Computer Interaction. Applicants should possess, or expect to obtain, at least a UK 2:1 honors degree or its international equivalent.
Submission or Application Guidelines
Interested candidates can apply by following the instructions provided at this link. Ensure that applications are submitted by the deadline for standard admissions, although late applications may be considered depending on funding.
Additional Context / Real-World Relevance
The increasing complexity of maritime operations necessitates a skilled workforce capable of managing advanced technologies onboard vessels. The research aims to bridge the gap between engineering and human factors, promoting a multidisciplinary approach that enhances crew training and operational safety. By addressing the psychological and physical demands placed on maritime crews, this research could significantly reduce the incidence of accidents, thereby saving lives and minimizing environmental impacts.
Conclusion
This PhD opportunity represents a critical step towards improving safety in the maritime industry through innovative research in machine learning. Prospective candidates are encouraged to apply and contribute to this vital field of study, which holds the potential to transform training and operational practices in maritime environments.
Category: PhD & Postdoc Positions
Tags: machine learning, human factors, human-computer interaction, maritime technology, fatigue detection, stress management, deep learning, ubiquitous computing, occupational health
