AI Research Scientist, ML and 3D Computer Vision
6 days ago
Individuals in this role are expected to be recognized experts in Artificial Intelligence and Machine Learning, particularly in areas such as DL, Computer Vision, Affective Computing, Behaviour Understanding, and Generative AI. As a Research Scientist, you will help us develop models, algorithms, datasets, and evaluation methodologies for the generation and animation of avatars to facilitate new and engaging user experiences, and apply them to large-scale production that has the potential to reach billions of users.
Responsibilities
Help to develop the next generation of GenAI models for avatar creation and animation Harness passion for technology and propose new research roadmaps which can lead to the creation of new models that can be applied to multiple applications Employ knowledge in the following areas: multi-modal data processing, affective computing, generative AI, and neural rendering Work towards long-term ambitious research goals, while identifying intermediate milestones Disseminate findings of research by publishing papers in high impact conferences and journals
Qualifications
PhD degree in Computer Science, Computer Engineering, Computer Graphics, Applied Sciences, Mathematics, Physics, or related field Experience in Python and PyTorch Track record of publishing papers in high-quality journals and conferences such as CVPR, ECCV/ICCV, SIGGRAPH, NIPS Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading conferences (e.g., SIGGRAPH, CVPR, ECCV, ICCV, NeurIPS, ICLR, ICML) or journals (e.g., PAMI, IJCV, JMLR, ToG) Experience designing and developing computer vision, computer graphics, or machine learning algorithms in C++ Experience with prototyping algorithms in Python Demonstrated software engineer and ML development experience via previous internships, work experiences, coding competitions, or widely used contributions in open source repositories (e.g. GitHub) Mathematical background and understanding of numerical optimization, linear algebra, probabilistic estimation and 3D geometry
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