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  Machine Learning for Autonomous Robot Exploration

   School of Engineering

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  Dr Pascal Meissner, Dr S S Aphale  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

A PhD project is available to work with Dr Pascal Meissner in the School of Engineering at the University of Aberdeen on one of today’s most relevant research problems at the intersection of autonomous robotics, machine learning, computer vision, and artificial intelligence.

Learn as you help robots see, learn, and navigate! As mobile robots spread into different areas of human activity, they are breaking through the boundaries of the structured and static environments, such as houses or gardens, where they were first used. This is particularly thanks to breakthroughs in deep learning, which have provided robots with an unprecedented level of flexibility and safety in navigating complex environments. However, these advances remain limited to environments through which such robots have previously been manually steered to painstakingly record 3D maps of them. Still, we need robots that can explore and monitor unknown environments (without having been steered through them first) to perform tasks in them. Giving robots such capabilities is the goal of this PhD and of utmost importance for the success of future robotic systems.

We will give you the opportunity to work independently and the freedom to steer the direction of the research according to your interests and strengths within the overall project goal.

You will be supervised by Dr Pascal Meissner and Dr Sumeet Aphale and will be a part of a cross-disciplinary team. As a member of our team, you will have unlimited access to our state-of-the-art robotics lab, as well as outstanding experts in autonomous robotics, machine learning, computer vision, artificial intelligence, human-robot-interaction, soft robotics, swarm robotics, industrial robotics, mechatronics, control engineering, and many more. Teamwork, diversity, and transparency belong to our core beliefs.

You will also have the opportunity to contribute to live industrial projects from time to time. In addition, senior PhD students are encouraged to take up paid teaching assistantships to develop their teaching skills, should they envision their futures in academia.

Aberdeen is ranked 8th in the UK for Electrical & Electronic Engineering (Complete University Guide 2021) which includes our team. The University is ranked in the top 20 in the UK (Guardian University Guide 2021) and in the top 180 in the world (Times Higher Education World University Rankings 2021).

Selection will be made on the basis of academic merit. The successful candidate should have, or expect to obtain, a UK Honours degree at 2.1 or above (or equivalent) in Computer Science, Electrical Engineering, Mechanical Engineering, Physics, Mathematics, or a related discipline. 

Applicants will have:

• Confidence and independence in programming complex systems (hands-on experience in software development)

• Solid background in algorithms and data structures

• Solid skills in maths (skills in statistics are helpful)

• Strong communication skills in English (both oral and written)

• Interest in autonomous robotics, machine learning, computer vision, or artificial intelligence

This position will be filled as soon as an appropriate candidate is found. Applicants are encouraged to contact Dr Pascal Meissner ([Email Address Removed]) to discuss their interest.


Applications can be completed online: and should include:

• All Degree Certificates/Academic Transcripts (officially translated into English and original). Please indicate your GPA

• A full CV describing your background (max 2 pages). Please indicate your relevant skills, scientific publications, awards, research videos and/or code, professional profile(s)

• A Motivation Letter / Research Statement describing your suitability for the PhD and research interests (max 2 pages)

• Any documents providing evidence of academic achievements, relevant practical experience, and qualifications earned at or outside of the university intended source of funding

Computer Science (8) Engineering (12) Mathematics (25)

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 THERE IS NO FUNDING ATTACHED TO THIS PROJECT.

Where will I study?

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