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Autonomous learning with Artificial Intelligence


Project Description

The ability to learn the sensorimotor maps of unknown environments without supervision is a vital capability of any autonomous agent, be it biological or artificial. An accurate sensorimotor map should be able to encode the agent’s world and equip it with the capability to anticipate or predict the results of its actions. However, to design a robust autonomous learning technique for an unknown, dynamic, partially observable or noisy environment remains a daunting task. This project will continue work in this area that focuses on developing solutions to the above problems, particularly with a focus on self-organising learning of an agent’s sensorimotor awareness in noisy environments.

This work has particular relevance in today’s world with the advent of self driving cars, and other potential uses for autonomous robots and intelligent vehicular motion.

Candidates should have (or expect to achieve) a UK honours degree at 2.1 or above (or equivalent) in Engineering, Physics or Computational Science.

It is essential that the successful applicant has a background in computer coding, algorithms and data analysis.

APPLICATION PROCEDURE:

• Apply for Degree of Doctor of Philosophy in Engineering
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV

Informal inquiries can be made to Dr A Starkey () with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ()

Funding Notes

This project is advertised in relation to the research areas of the discipline of Engineering. The successful applicant will be expected to provide the funding for Tuition fees, living expenses and maintenance. Details of the cost of study can be found by visiting View Website. THERE IS NO FUNDING ATTACHED TO THIS PROJECT.

How good is research at Aberdeen University in General Engineering?

FTE Category A staff submitted: 38.60

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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