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Research Fellow (Postdoc) in Creative Technologies2017 October 25

 

Post Summary

 

The post will research, develop, pilot and demonstrate a set of professional tools and techniques for making content ‘smarter’, so that it is fully adaptive in a broad, unprecedented manner: adaptive to context (which facilitates re-use), to purpose (among or within industries), to the user (improving the viewing experience), and to the production environment (so that it is ‘future-proof’). The approach is based on research into computer animation; automated classification and tagging using deep learning and semantic labelling to describe and draw inferences; and the development of tools for automated asset transformation, smart animation, storage and retrieval. These new technologies and tools will show that a vast reduction of costs and increases in efficiency are possible, facilitating the production of more content, of higher quality and creativity, for the benefit of the competitiveness of the European creative industries.

Specifically, the position will help to research and develop:

  • A framework and tools for automatically classifying, validating and finding smart assets, using deep learning and semantic labelling techniques 3D data.
  • A framework and tools for the automatic transformation and adaptation of smart assets to new contexts, purposes, users and environments, and for the synthesis of new smart assets from existing ones.
  • Real-time control systems for authoring animated content using smart assets, automatically synthesizing new scenes from existing ones and integrating smart assets into virtual production scenarios.

 

Standard Duties and Responsibilities of the Post

The role of the candidate will be to:

  • Develop algorithms for populating crowded scenes based on previously created examples. It will involve close collaboration with other consortium partners on developing assets descriptors using Deep Learning, and using those to populate new scenes with naturally behaving characters.
  • Fundamental and/or applied research in Computer Animation at the intersection of Crowd Simulation, Character Animation and Deep Learning
  • Scientific publications
  • Contribution to prototype and demonstrator development
  • Overall contribution to SAUCE and teamwork
  • Supervision of PhD and other students
  • Outreach & dissemination

 

Funding Information

This project is funded as part of the Horizon 2020 – the Framework Programme for Research and Innovation (2014-2020).

Person Specification

 

Qualifications

The candidate should have a Ph.D. degree in Computer Science, Engineering, or a related field in the area of ICT with a focus on computer animation. Experience in the area of crowd simulation or character animation is highly desirable. 

 

Knowledge & Experience (Essential & Desirable)

  • An established track record of publication in leading journals and/or conferences, in one or more sub-areas of Computer Animation.
  • Excellent knowledge of and integration in the related scientific communities.
  • The ability to work well in a group, and the ability to mentor junior researchers, such as Ph.D. students.
  • Knowledge of Machine Learning and Deep Learning is highly desirable. 
  • Experience with motion capture is welcome.

Skills & Competencies

  • Good written and oral proficiency in English (essential).
  • Effective communication and interpersonal skills both written and verbal.
  • Proven aptitude for Programming, System Analysis and Design.
  • Proven ability to prioritise workload and work to exacting deadlines. 
  • Proven track record of publication in high-quality venues. 
  • Flexible and adaptable in responding to stakeholder needs. 
  • Dedicated team player who can take responsibility to contribute to the overall success of the team.
  • Enthusiastic and structured approach to research and development.
  • Excellent problem-solving abilities. - Desire to learn about new products, technologies and keep abreast of new product and technical and research developments.

 

Further Information for Applicants
Informal enquiries to:

Professor Aljosa Smolic: smolica@scss.tcd.ie


To Apply:
To apply please email a brief cover letter describing relevant experience and a PDF copy of

your CV along with names and contact information for 2 referees to smolica@scss.tcd.ie



 

https://v-sense.scss.tcd.ie/

 

www.scss.tcd.ie/

 

https://www.tcd.ie/hr/