PhD Studentship: Model Reconstruction: The University of Southampton’s School of Electronics and Computer Science is offering a PhD Studentship for research in AI-based multi-modal 3D environment model reconstruction. This opportunity presents a chance to delve into the forefront of computer vision and artificial intelligence, contributing to the advancement of scene understanding crucial for various applications including robotics, telecommunication, healthcare, and more.
PhD Studentship: AI-based Multi-modal 3D Environment Model Reconstruction
Summary Table:
- Designation: PhD Studentship
- Research Area: AI-based Multi-modal 3D Environment Model Reconstruction
- Location: University of Southampton, Southampton
- Eligibility/Qualification: First class honours degree (or equivalent) in relevant field; proficiency in Python; experience in computer vision and machine learning preferred; additional criteria for non-native English speakers.
- Funding: University DTP Scholarship available for UK home students; self-funding option open for international students.
- Hours: Full Time
- Placed On: 30th May 2024
- Closes: 19th June 2024
Supervisory Team: Dr. Hansung Kim
Project Description: Computer Vision, a dynamic field within artificial intelligence, is the focus of this project, aiming to develop a pipeline for modeling and rendering complete environments using multi-modal inputs such as video, audio, and text. Joining a team of dedicated researchers, you will explore topics in AI-based multi-modal 3D environment model reconstruction.
How to Apply:
Interested candidates should directly contact Dr. Hansung Kim (h.kim@soton.ac.uk) with their CV, Degree Transcripts, and a brief introduction. Dr. Kim will arrange interviews and guide successful applicants through the official application process.
Last Date for Apply: 19th June 2024
This is an exciting opportunity to be at the forefront of cutting-edge research in artificial intelligence and computer vision. Don’t miss your chance to contribute to groundbreaking advancements in scene understanding and apply your skills in a vibrant research environment.
