Research Associate in Non-Destructive Evaluation Automation (794647)

The advertised position will be primarily focused on the Frontiers in Electromagnetic Non-Destructive Evaluation (NDE) Research (FENDER) project, which involves several universities and 20+ companies. FENDER aims to bring game-changing ideas to electromagnetic non-destructive evaluation by harnessing advances in electronics, signal processing, modelling, and data science, positioning electromagnetic sensing at the heart of future Industry 4.0 manufacturing, advanced materials processing, and circular-economy technologies.

The Research Associate will lead and deliver robotic electromagnetic non-destructive evaluation (EM NDE), with a particular focus on integrating machine learning into autonomous robotic inspection workflows. Key responsibilities include developing robotic EM NDE systems that combine sensor-driven control, adaptive path planning and real-time Machine Learning (ML)-enabled data interpretation for defect detection and characterisation. The postholder will implement and deploy machine learning models for processing spatially encoded EM NDE data, fusing inspection signals with robot pose information to enable intelligent decision-making during inspection. They will contribute to the design and validation of autonomous inspection demonstrators, undertake experimental studies in laboratory and industrial environments, and work closely with academic and industrial partners to translate ML-driven robotic NDE research into deployable solutions. The role also includes contributing to publications, technical reports and project coordination activities.

The successful candidate will have strong expertise in robotics, automation or related engineering disciplines, with demonstrable experience in applying machine learning to sensor data or robotic systems. Essential skills include proficiency in programming (e.g. Python and/or C++), experience with robotic middleware such as ROS, experience of working with different robotic platforms (Kuka, UR, Fanuc, etc.) and practical knowledge of machine learning techniques for signal processing, pattern recognition or data-driven modelling. Experience with real-time or near-real-time ML deployment, data fusion, or ML-assisted perception and control is highly desirable. Familiarity with electromagnetic sensing or NDE, and experience working with experimental datasets and physical systems, would be a strong advantage. The candidate should be able to work independently, integrate ML methods with hardware and robotic platforms, and collaborate effectively within multidisciplinary research teams spanning robotics, sensing and data science.

As a Research Associate, under the general guidance of a research leader, you will develop research objectives and proposals, play a lead role in relation to a specific project/s or part of a broader project, conduct individual and/or collaborative research, contribute to the development of new research methods, identify sources of funding, and contribute to the securing of funds for research, including drafting grant proposals and planning for future proposals. You will write up research work for publication, individually or in collaboration with colleagues, and disseminate the results via peer reviewed journal publications and presentation at conferences. You will join external networks to share information and ideas, inform the development of research objectives and to identify potential sources of funding. You will collaborate with colleagues to ensure that research advances inform departmental teaching effort, and you will collaborate with colleagues on the development of knowledge exchange activities by, for example, participating in initiatives which establish research links with industry and influence public policy and the professions.

Informal enquiries about the post can be directed to Dr Ehsan Mohseni, Senior Lecturer in Applied Sensing and Signal Processing (ehsan.mohseni@strath.ac.uk).

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