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Artificial Intelligence and Deep Learning in Robotics and Autonomous Systems


   School of Science, Engineering and Environment

  Prof William Holderbaum  Applications accepted all year round  Self-Funded PhD Students Only

Manchester United Kingdom Artificial Intelligence Control Systems Data Science Engineering Mathematics Machine Learning Mathematical Modelling Mechatronics Robotics

About the Project

Robots as well as Autonomous Vehicle are always very challenging to control due to external forces acting on them and unpredicted or unseen environment. These forces are associated with disturbances which make Robots and Autonomous Vehicle difficult to control. In unseen environment the Robots and Autonomous Vehicle are associated with maps. The purpose of this research project is to use AI (Artificial Intelligence) methods such as reinforcement learning algorithm and control systems based vision. The PhD project will also look at developing techniques based on Deep learning, mathematical modelling, Control theory and Machine learning, and compare with Q-learning, hindsight replay; Neural Network autonomous landing combine with learning environment is a potential avenue to the research.

Reinforcement learning is a powerful, overall approach to discovering optimal policies for complex decision-making problems. Recently, with approximations of functions such as neural networks, Reinforcement learning has significantly expanded the range of applications, from playing computer games to learning movements by simulated human. It is successfully used in areas where massive amounts of simulated data can be generated, such as robotics and games. Reinforced learning as a promising path to General AI.

Other application of reinforcement learning in this studies is on robot manipulation with an interest in real-world robot learning problems, which deal of noisy data, and the practical aspects of real-world data gathering rather than simulated tasks. There is a particular interest to focus on methods which combine learning-based methods (such as imitation learning and reinforcement learning) with classical methods (such as optimal control and vision-based state estimation). The PhD projects is at the intersection of learning-based methods (such as imitation learning and reinforcement learning) and classical methods (such as optimal control and vision-based state estimation).

Person Specification:

The successful applicant should have been awarded, or expect to achieve, a Bachelors or a Masters degree in a relevant subject with a 60% or higher weighted average, and/or a First or Upper Second Class Honours degree (or an equivalent qualification from an overseas institution) in Control Engineering; Robotics and Computer Science. Preferred skill requirements include knowledge/experience of Data Science, Control Systems; Mathematical Modelling, Artificial Intelligence and Machine Learning.

For informal enquiries contact Prof William Holderbaum

How to apply:

Submit a formal application at this link: http://webapps.ascentone.com/Login.aspx?key=5d4b012a-bb6c-495b-b2e4-b5a56b3ccf00 

 You will need to have the following documents ready to upload to the application site:


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