PhD Studentship in 3D Semantic Scene understanding with Computer Vision and Artificial Intelligence
University of Southampton School of Electronics and Computer Science
United Kingdom

PhD Studentship - 3D semantic scene understanding with Computer Vision and Artificial Intelligence

Vision, Learning and Control
Location: Highfield Campus
Closing Date: Tuesday 11 May 2021
Reference: 1355421FP
Supervisory Team: Dr. Hansung Kim

Project description

Computer Vision is one of the most active areas where artificial intelligence (AI) is being used. This area is extremely expanding and getting a lot of interests and investments these days. Taking advantages of recent advancements in deep learning, active perception of an environment or human behaviours have shown significant improvement in building new knowledge and practical applications in our daily life.

The University of Southampton is ranked in the top 1% of universities worldwide, and the School of Electronics and Computer Science (ECS) is the leading department of its kind in the UK with a long tradition of interdisciplinary research. We actively encourage and support our PhD students to pursue their research and other activity within their fields of interest.

Project Description

Visual scene understanding studies the task of representing a captured scene in a manner emulating human-like understanding of that space. Attaining this understanding is crucial for applications such as entertainment, robotics, smart home, security systems, healthcare, assisted living, etc. The student will investigate indoor 3D scene reconstruction and understanding challenges using state-of-the-art Ai technologies, aiming to demonstrate its potential contributions, applications, and versatility. Various vision sensors can be considered such as normal cameras, depth cameras and 360 spherical cameras. There can be a chance of collaborative research with BBC R&D or Institute of Sound and Vibration Research (ISVR) for application development in audio-visual scene understanding and reproduction.

Requirements

All applicants are required to have a first or upper-second class honours degree (or equivalent). Candidate without a MSc or MEng in computer vision, computer graphics, machine learning or applied mathematics would have to provide strong justification that they would be able to handle the maths and programming necessary to complete a PhD in this field.

The essential selection criteria include:

- Prior knowledge in the areas of computer vision and machine learning (especially deep learning).

- Proficiency in one or more of C++ / Python / Matlab

- Ability to work independently or as part of a team.

The desirable selection criteria include:

- Project experience or working experience in the areas of computer vision and machine learning (especially deep learning)

- Experience in camera systems (vision camera, Kinect, 360 cameras, etc.).

Funding Opportunities

For UK home students (UK National or settled status), DTP scholarship (3.5-year full tuition fee and stipend £15,609 tax-free per annum) is offered. I also welcome self-funded international students with strong research background.

Application

Please email (h.kim@soton.ac.uk) with your CV ASAP before you officially apply to the university to save time and adjust our schedule.

If you wish to discuss any details of the project informally, please contact Dr. Hansung Kim, VLC/ECS Research Group, Email: h.kim@soton.ac.uk

Closing date: 11 May 2021 for standard admissions, but later applications may be considered depending on the funds remaining in place.

How To Apply

Applications should be made online. Select programme type (Research), 2021/22, Faculty of Physical Sciences and Engineering, next page select “PhD Computer Science (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Hansung Kim

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts to date

Apply online:

For further information please contact: feps-pgr-apply@soton.ac.uk

 


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