Artificial Intelligence and Machine Learning

Program Overview

We are excited to offer this new research training programme for graduates who want to become Artificial Intelligence researchers and innovators. If you are enthusiastic to become an Artificial Intelligence researcher and innovator who would want to learn to develop novel and innovative AI for a custom problem, then this programme is for you.

With artificial intelligence now embedded in many areas of business and public service, people are needed who combine theoretical grounding in AI with the ability to imagine, lead and deliver Research & Development (R&D) projects that meet regulatory and real-world performance expectations. This MRes programme gives the opportunity to gain these skills, for a wide range of industries and careers, and we offer projects in various domains such as health, business and finance, communications, and energy/product supply systems.

The MRes programme is a one-year, full-time programme leading to the MRes award. It is designed to provide focused AI training and a high-level supervised research project that will result in developing high-level analytical skills and a broad range of competencies. The MRes also involves taught-module lectures and assessments in the first and second terms, combined with initial work on the main project, followed by full-time work on the project until 30 September, with the individual MRes thesis being submitted towards the end. The research project is normally supervised by one AI expert and often co-supervised with an expert in an application area.  Some students may also have a co-supervisor from the industry. MRes award holders would be able to, for example:

Apply broad knowledge of state-of-the-art AI and machine learning to critically assess the strengths and weaknesses of a range of research and innovation approaches. 

Apply the principles of the law as well as an understanding of responsible research and innovation, data protection, ethics and bias relevant to AI research and innovation. 

Create software for advanced AI and machine learning using appropriate computing languages (e.g. Python) and frameworks (e.g. PyTorch, Tensorflow). 

Identify key advances, uncertainties and opportunities in AI methods and the evidence on organisational, business and human factors for applications.