PhD Candidate in Automated Optical Assessment of Marine Biofouling

Job summary

This PhD project focuses on developing automated optical methods for assessing marine biofouling using advanced imaging and machine learning techniques. The research will take place at the CoaST Maritime Test Centre, where the candidate will work with underwater camera systems and large datasets to improve fouling detection and quantification.

Qualifications

• Strong programming skills, preferably in Python
• Experience in image analysis, computer vision, scientific imaging, or signal processing
• Interest in machine learning and data-driven research
• Practical, hands-on mindset with motivation for experimental work
• Ability to work independently and collaboratively
• Good communication skills in English
• A two-year master's degree (120 ECTS points) or equivalent in an engineering discipline (e.g., mechanical or chemical engineering)

Responsibilities

• Develop and enhance automated image analysis workflows for biofouling assessment
• Process large image datasets from underwater tests
• Apply and develop machine learning methods for fouling detection
• Improve robustness and quality control of analysis pipelines
• Contribute to the development and maintenance of underwater imaging systems
• Explore new methods for early detection of biofouling
• Study biofouling processes under realistic conditions
• Collaborate with researchers and students across various disciplines
• Disseminate findings through publications and presentations
• Supervise BSc and MSc student projects
• Complete PhD courses and teaching assistant work

Education

• A master's degree in an engineering discipline is required.

Tools

• Underwater camera systems, image analysis software, machine learning frameworks.