Computational Engineering

Course Overview

We are in the next Industrial revolution and the challenge faced by graduates is to succeed in an age of automation. There is no aspect of modern life unaltered by information processing engines. Examples include teaching (online learning and assessment), automotive (driverless cars and electronic control systems), the economy (high speed automated trading), shopping and entertainment (virtual assistants and recommendation engines), health (automated diagnostics and non-invasive body scanning) and the digital humanities.

The principles enabling the design of this new wave of products are embodied in the discipline of Information Engineering.



This one year full-time (or between two and three year part-time) course provides graduate engineers with the skills to design modern computational products and systems. Graduates specialise in fundamental theory and applications relating to the generation, distribution, analysis, and information use in engineering / science.



This programme is a strand of the M.Sc. in Electronic Information Engineering programme. Computational Engineering has evolved to encompass all aspects of simulation and design in the Engineering disciplines. All physical simulation tools rely on algorithms developed from a knowledge of computational techniques from graphics engines to financial prediction to fluid mechanics to electric power distribution. In this course students are equipped with the knowledge to apply computational techniques in various domains.



Is This the Course For Me?

The programme is designed for individuals with a strong academic background in electronic and electrical engineering, computer engineering, or related fields, and who wish to specialise and advance their knowledge in electronic and information engineering. It will suit individuals interested in pursuing a career in areas such as telecommunications, wireless communications, data communications, computer networks, software engineering, and signal processing. It is also suitable for individuals already working in the industry and wish to enhance their skills and knowledge to progress in their career.



Course Structure

This course can be taken as either one year full-time or over two to three years part-time, and consists of taught modules worth 60 ECTS and a project worth 30 ECTS. The Computational Engineering specialism in is available for students selecting at least 15 ECTs from the Computational Engineering strand. Masters candidates complete a substantial project and submit a report which accounts for 30 ECTS.



In addition to direct entry to the Masters programme, parallel Postgraduate Certificate (30 ECTS) and Postgraduate Diploma (60 ECTS) entry routes are available for direct separate application. For students who successfully complete the certificate and diploma, there is an option to rescind these awards and apply to complete an M.Sc. in Computational Engineering. Part-time students may follow the staged award path over three years of study with a possible gap of up to one year in between.

Course Content

All students aiming for a Masters qualification are required to take the following core modules: Research Dissertation; Research Methods; Computational Methods; and Introduction to Deep Learning.



In the Computational Engineering strand, students must select additional electives: Algorithms for Quantum Computing; Cyber-Physical systems and control; Simulation for Geophysical Modelling and Computation for Transportation Engineering.

Admission Requirements

Admission is typically restricted to graduates who have achieved an upper second-class Honours degree (2.1), or higher, in Engineering, Science, Computing, Statistics, Mathematics or a related discipline. Well-qualified candidates or industry professionals from other disciplines who have sufficient knowledge of computational aspects of engineering and science may also be considered.



English Language Requirements

All applicants to Trinity are required to provide official evidence of proficiency in the English language. Applicants to this course are required to meet Band B (Standard Entry) English language requirements. For more details of qualifications that meet B and B, see the English Language Requirements page.

Closing Date 31st July 2025

1 Year Full-Time

Next Intake September 2025

Career Oportunities

While some of our students choose to undertake Ph.D. research, most of our graduates enter the job market after qualifying. They have gained employment in industries including digital assistive technology (speech recognition and synthesis), automotive systems (remote sensing and cyber physical control), economics (quantitative automated trading), entertainment (audio-video streaming and cinema visual effects), health (medical imaging) and computational science and engineering.