Entry requirements

A 2:2 BEng honours degree, or international equivalent, in:

electrical and electronic engineering
computer engineering
mechanical engineering
physics communications, information and control engineering
electronics information and science technology

Months of entry

September

Course content

This course equips you with the theory and practice necessary to begin a career as a design or development engineer in this exciting field.

Overview

This unique MSc draws on our research strengths in communications and digital signal processing. You’ll gain a deep understanding of theory and real-world application. Course highlights:

coverage of all major areas in communications engineering and signal analysis
a balanced mix of theory and hands-on learning
innovative teaching that builds practical, industry-relevant knowledge
training in design, problem-solving, and research methods
transferable skills in analysis, research, and technical communication

This course is designed for both practising professionals and graduates in:

electrical and electronic engineering
computer engineering
mechanical engineering
physics
communications, information and control engineering
electronics
information and science technology

What you'll learn

Through compulsory modules you'll learn:

mathematical foundations and fundamental concepts of machine learning
formulating designing, testing, and validating different types of machine learning algorithms
ethical considerations, as well as issues regarding privacy and security
issues relating to modern telecommunications systems, protocols, flow and error control
security and encryption and their importance in modern communication systems
the principles of wireless and broadband communication networks
recent developments in cellular mobile systems and access technology
fundamental concepts of multimodality sensing, data acquisition and their applications including data fusion and visualisation
detailed knowledge of the simulation techniques for communications systems and other signal processing problems
in-depth knowledge of the architecture of DSP devices and communications hardware
emerging methods for the manipulation and analysis of single, multi-dimensional and random signals
how to conduct case studies in biomedical and healthcare applications
discrete-time signal processing algorithms and approaches to measure deterministic and random signals in frequency domain