10155 - Research Associate
| Posting date: | 10 April 2024 |
|---|---|
| Salary: | £39,347 to £46,974 per year |
| Hours: | Full time |
| Closing date: | 01 May 2024 |
| Location: | Edinburgh, Scotland |
| Company: | University of Edinburgh |
| Job type: | Temporary |
| Job reference: | 10155 |
Summary
UOE07 - £39,347 to £46,974 per annum
School of Informatics
Contract Type - Fixed Term - 4.5 Years
Full Time - 35 Hours Per Week
The Opportunity:
The post is full-time (35 hours per week) and fixed term for 4.5 years.
The successful candidate will be supervised by Dr Changjian Li (with a dotted line to Dr Laura Sevilla-Lara, and Prof Melissa Terras). In collaboration with other researchers at Abertay University, the team will study generative AI methods to design new machine learning systems to enhance the current workflows of the creative industry, from content creation (2D and 3D), exploration to use (animation, simulation). The successful candidate will also have the opportunity to work with world-leading industry partners in gaming, animation and film sectors by discovering new research topics, developing thoughtful integration of generative AI into virtual production and performance pipelines, and accessing real industrial data as a unique advantage.
About the CoSTAR Realtime Lab:
The CoSTAR Realtime Lab will bring together video games development expertise in Dundee’s globally significant InGAME cluster with world-leading applied research and development (R&D) at Abertay, with the University of Edinburgh’s expertise in machine learning and artificial intelligence.
The Realtime Lab will place R&D side-by-side with live commercial production, offering routes to commercialization and markets beyond most small and medium business (SME) networks. It will facilitate industry sector engagement, catalyzing innovation in:
performance and motion capture
virtual humans and dynamic procedural performance
machine learning for production and artificial intelligence for process reproduction
artificial intelligence for dynamic effects and procedural graphics for visual effects
advanced scanning technologies for 3D volume acquisition and ML/AI for procedural environment generation
developing lighting and ray tracing standards to deliver environmental fidelity
location-based software and hardware integration
integrated virtual and real-world film and TV production.
Your skills and attributes for success:
Ph.D. degree (or nearing completion)
A strong background in computer graphics and 3D vision, and research expertise in generative AI, e.g., diffusion models, and radiance fields
Publications at top venues in CG, CV or/and ML (e.g., SIGGRAPH, SIGGRAH Asia, TOG, Eurograhics, CVPR, ECCV, ICCV, NeurIPS, ICLR, ICML, ...)
Strong programming skills (e.g., Python, C++)
Experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow)
Strong communication, presentation, and writing skills, and excellent command of English
Ability to work effectively as part of a team, to meet deadlines and to report on project progress.
To apply please include:
a cover letter (describing how past experience and future plans fit with the advertised position)
complete CV, including list of publications
the names and email addresses of two referees.
Contact details for enquiries: Dr Changjian Li, Changjian.li@ed.ac.uk (https://enigma-li.github.io/ ) and Prof. Melissa Terras, m.terras@ed.ac.uk
School of Informatics
Contract Type - Fixed Term - 4.5 Years
Full Time - 35 Hours Per Week
The Opportunity:
The post is full-time (35 hours per week) and fixed term for 4.5 years.
The successful candidate will be supervised by Dr Changjian Li (with a dotted line to Dr Laura Sevilla-Lara, and Prof Melissa Terras). In collaboration with other researchers at Abertay University, the team will study generative AI methods to design new machine learning systems to enhance the current workflows of the creative industry, from content creation (2D and 3D), exploration to use (animation, simulation). The successful candidate will also have the opportunity to work with world-leading industry partners in gaming, animation and film sectors by discovering new research topics, developing thoughtful integration of generative AI into virtual production and performance pipelines, and accessing real industrial data as a unique advantage.
About the CoSTAR Realtime Lab:
The CoSTAR Realtime Lab will bring together video games development expertise in Dundee’s globally significant InGAME cluster with world-leading applied research and development (R&D) at Abertay, with the University of Edinburgh’s expertise in machine learning and artificial intelligence.
The Realtime Lab will place R&D side-by-side with live commercial production, offering routes to commercialization and markets beyond most small and medium business (SME) networks. It will facilitate industry sector engagement, catalyzing innovation in:
performance and motion capture
virtual humans and dynamic procedural performance
machine learning for production and artificial intelligence for process reproduction
artificial intelligence for dynamic effects and procedural graphics for visual effects
advanced scanning technologies for 3D volume acquisition and ML/AI for procedural environment generation
developing lighting and ray tracing standards to deliver environmental fidelity
location-based software and hardware integration
integrated virtual and real-world film and TV production.
Your skills and attributes for success:
Ph.D. degree (or nearing completion)
A strong background in computer graphics and 3D vision, and research expertise in generative AI, e.g., diffusion models, and radiance fields
Publications at top venues in CG, CV or/and ML (e.g., SIGGRAPH, SIGGRAH Asia, TOG, Eurograhics, CVPR, ECCV, ICCV, NeurIPS, ICLR, ICML, ...)
Strong programming skills (e.g., Python, C++)
Experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow)
Strong communication, presentation, and writing skills, and excellent command of English
Ability to work effectively as part of a team, to meet deadlines and to report on project progress.
To apply please include:
a cover letter (describing how past experience and future plans fit with the advertised position)
complete CV, including list of publications
the names and email addresses of two referees.
Contact details for enquiries: Dr Changjian Li, Changjian.li@ed.ac.uk (https://enigma-li.github.io/ ) and Prof. Melissa Terras, m.terras@ed.ac.uk