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Job Description:
TCOMS is seeking a highly motivated and technically proficient candidate to join its team in the development of a digital twin-enabled Infrastructure Health Monitoring System for Offshore and Coastal Infrastructure. The successful candidate will play a key role in the development of hybrid modeling and predictive analytics frameworks to assess infrastructure integrity and settlement, supporting proactive maintenance, early warning, and long-term resilience of marine assets.
Key Responsibilities: · Contribute to the development of a digital twin-enabled Infrastructure Health Monitoring framework combining multi-sensor data, numerical models, and AI for marine structures.
· Apply model updating and data assimilation techniques to estimate key parameters and improve prediction accuracy.
· Build probabilistic prediction systems to assess structural health or settlement of marine structures, accounting for uncertainty in decision-making.
· Design and train machine learning / deep learning / physics-informed models to predict structural responses (e.g. dynamic responses for offshore structures, settlement behaviors for coastal protection infrastructures) using data from multiple sources, considering external factors / coastal processes, e.g. waves, wind, current, and tides.
· Validate the system through field deployment at a Proof-of-Concept site and benchmark performance.
· Collaborate closely with interdisciplinary teams to deliver impactful technical outcomes and peer-reviewed publications.
Requirements:
· PhD degree in Coastal Engineering, Ocean Engineering, Civil Engineering, Applied Mathematics, or a related discipline.
· Strong foundation in coastal or ocean engineering, with knowledge of wave-structure interaction, tidal influences, and soil-water-structure response in nearshore / offshore environments.
· Proficiency in data assimilation techniques, including Bayesian inference, Kalman filtering and its variants, and model updating, with demonstrated application to engineering systems.
· Experience in developing data-driven models (machine learning, deep learning) and/or physics-informed models for structural or geospatial monitoring problems.
· Familiarity with coastal and offshore infrastructure systems, such as seawalls, breakwaters, quay walls, floating platforms, or station-keeping systems is an advantage.
· Proficiency in scientific programming using Python and/or MATLAB for data analysis, model development, and AI workflows.
· Proven ability to work both independently and collaboratively in interdisciplinary teams.
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