Computer Vision Engineering (KTP Associate)

PURPOSE OF POST
– Transfer knowledge of data science and state-of the art machine learning techniques to Fennex.
– Take a leading role in developing a set of methods for exploration, visualising, pre-processing and handling large volumes of Videos related to safety in the Oil and Gas Sector.
– Take a leading role in designing, implementing, and evaluating a set of intelligent and data-driven methods for recognising and anticipating specific actions within videos.
– Create and evaluate a range of anomaly detection methods using state-of-the-art machine learning and deep learning models.-
– Create and develop tools to embed the above directly into one of Fennex’s HSE solutions (Microsoft, Azure based)
– Develop technical and personal skills (verbal and written) to meet the requirements of increasing responsibility and experience level.

PRINCIPAL DUTIES
– Undertake an in-depth and critical literature review in computer vision, anomaly detection, and human behaviour and actions within a working environment.
– Take a leading role in developing and evaluating AI models for solving complex computer vision tasks.
– Any other duties that maybe reasonable, assigned by the Academic Supervisor/ Company Supervisory teams.

The candidate should be self-motivated with an ability to work independently and to tight deadlines within a dynamic and small team environment. In addition, they must have strong programming skills as well as a genuine enthusiasm for applying advanced methods to a real-world problem. Strong knowledge and understanding of programming languages such as R, Python, or similar language is essential. Experience with modern deep learning frameworks such as PyTorch or Tensorflow is a must.

To develop an intelligent hazard detection solution powered by AI and computer vision technology, to accurately detect and predict hazards in complex offshore operations, and to enable bespoke product capabilities for market expansion internationally and across the Energy Industry.