Artificial Intelligence and machine learning for cutting edge problems

Modern Artificial Intelligence (AI) is an emerging field that develops intelligent algorithms and machines that include: self-driving cars, smart cameras, surveillance systems, robotic manufacturing, eHealth, emotion intelligence, and product recommendations. Modern AI is based on machine learning techniques that can improve their performance by learning from experience. Experience often comes in the form of very large amounts of data. The resulting algorithms and models are used for making predictions and for improving decisions.

These studentship will focus on emerging machine learning techniques especially focus on Deep Learning abstraction designs to interpret real-time heterogeneous large scale data. This is a novel area of computer science that encompasses a multi-disciplinary working. The project can be categorised in three main research intensive components:

• Formulate and assemble heterogeneous large scale datasets for machine learning;
• Realise and deploy machine learning based on Deep Learning intelligence algorithms on the datasets;
• Develop and validate visual analytic tools for interactive human-in-the-loop learning processes.

These studentships will cultivate and lead to entirely new interactive visualization tools to enable exploration of real-time heterogeneous large scale data.

This project has guaranteed funding for three years. The project will pave the way for a high quality PhD thesis supported by impacting publications in top journals and conferences. The supervisory team comprises UWS academics. Their expertise in these research topics together with the motivation and involvement of the candidate will guarantee the success of the project. The final outcome will be a real time concept demonstrator to show the university research standing and help to get external funding.