Electrical Power Engineering (September Start) Northumbria University

Overview

This Electrical Power Engineering (September Start) programme at Northumbria University   programme continually has a very high pass and student satisfaction rate, leading to numerous Ph.D. studies and employment.

You will develop cross-discipline skills through multidisciplinary group work and an individual dissertation. You will learn essential skills such as information research, project planning and management, cost analysis, and budgeting. You will be actively involved within the Faculty Research Seminars, where you can broaden awareness and views across different research areas. 

You’ll benefit from the University’s excellent facilities that include specialist electrical and electronics laboratory resources. Northumbria has a well-established reputation for producing graduates who can apply their knowledge to generate creative solutions for sustainable electrical power systems.

On successfully completing the programme, you will gain an understanding and comprehensive knowledge of current practice and new technological developments.

Your Future

By the end of this course you’ll be in an excellent position to start or continue a career in electrical power engineering and/or the renewable energy industry. Roles could include designing, developing and maintaining electrical control systems and components.

You could also undertake a postgraduate research degree such as an MPhil, PhD and Professional Doctorate. If you decide to start up your own business, it’s good to know that the combined turnover of our graduates’ start-up companies is higher than that of any other UK university.

Whatever you decide to do, you will have the transferable skills that employers expect from a master’s graduate from Northumbria University. These include the ability to tackle complex issues through conceptualisation and undertaking research, the ability to contribute to new processes and knowledge, and the ability to formulate balanced judgements when considering incomplete or ambiguous data.