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Real Students. Real Journeys. Real Outcomes.

The University of Trinidad and Tobago (UTT) continues to transform lives through practical, industry-driven education. Trudy Daisley, former military veteran, mother and content creator, is a powerful example of that impact.

Dr Deanesh Ramsewak of The University of Trinidad and Tobago's (UTT) Centre for Maritime and Ocean Studies (Marine Sciences Unit), was awarded a...

Growing up, Ms. Thomas was fascinated by nature documentaries and the diversity of marine life. Visits to the beach, especially Down the Islands and the Bon Accord Lagoon, Tobago, captivated her with their vibrant coral reefs and diverse species...

The steelpan is a percussive musical instrument that originated in Trinidad and Tobago in the 1930s. The advancement and influence of digital technology has impacted the design of many new musical instruments including the traditional steel pan, with digital versions of the instrument now available such as...

In a recently published Viewpoint article in the Caribbean Medical Journal, Dr. Umesh Persad, Associate Professor - Mechanical Engineering, Manufacturing, and Entrepreneurship (MME) Unit at The University of Trinidad and Tobago (UTT), and Dr. Marlon Mencia, Lecturer in Trauma and Orthopaedics at...

The use of 3D-printed anatomical models to visualise complex pathology is becoming more commonplace in orthopaedic surgical practice. 3D-printed models are created from high resolution patient scan data (such as CT and MRI) and can be used for preoperative planning, collaboration within the surgical team and as an aid to communication with the patient during consultation.

This paper describes a study of the new product development process within the food and beverage manufacturing sector of Trinidad and Tobago. A total of twenty-three (23) companies agreed to participate in the study.

The results demonstrate the usefulness of the TDA approach using machine learning and network visualisation to explore and extract insight from user capability data. The machine learning approach shows promise for application in future Ergonomics/Human Factors studies that capture large multivariate data sets. The Better Design pilot study points the way to future large-scale data collection efforts with multiple sensory, cognitive and motor variables.