MEng Computer Engineering (with Year in Industry)
Entry requirements
A level: AAA including Mathematics and and at least one from Biology, Chemistry, Computing, Digital Technology, Electronics, Further Mathematics, Geography, ICT [not Applied ICT], Physics, Software Systems Development or Technology and Design. IB: 36 points overall, including 6,6,6 at Higher Level, including Mathematics and a relevant Science
About this course
MEng in Electrical and Electronic Engineering and Software Engineering, blending hardware design with software development. Core topics include Programming, Systems Design, Circuit Design, Computer Architecture, Embedded Systems, Signal Processing, Microelectronics, Control Systems, Data Analysis, Statistics, Mathematical Modelling, Ethics & Professional Practice, Entrepreneurship, Human Behaviour, and Artificial Intelligence & Machine Learning. This undergraduate programme features industry placements, company-sponsored hackathons, and project challenges integrated into the curriculum.
Modules
- Mathematics 1
- Embedded Systems
- Object Oriented Programming
- Digital Systems
- Fundamentals of Electric Circuits
- Signals and Communications
- Embedded Systems 2
- Mathematics
- Data Structures and Algorithms
- Employability Skills and Placement Preparation
- Systems Security and Cryptography
- Introduction to Artificial Intelligence and Machine Learning
- Signals and Control
- Digital Systems
- Communications
- Electronics and Circuits
- Sandwich - Year of Professional Experience
- Engineering Entrepreneurship
- Concurrent Programming
- Connected Health
- Networks and Communications Protocols
- Signal Processing and Communications
- Control Systems Engineering
- Malware Analysis
- Advanced Electronics
- Deep Learning
- Video Analytics and Machine Learning
- Project 4
- Algorithms: Analysis and Application
- Wireless Communications
- Wireless Sensor Systems
- Advanced Computer Engineering
- Control methods for Cyber-Physical Systems
- Robotics and Intelligent Systems
- Fairness, Interpretability and Privacy in Machine Learning
- Parallel and Distributed Computing
- Energy-Efficient Computing