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Join a strong research team advancing human‑centric computer vision using state‑of‑the‑art, transformer‑based models and a high‑fidelity synthetic data generation pipeline. You’ll work on unified approaches to monocular and multi‑view human reconstruction, with opportunities to explore models that operate consistently across hands, faces, and full bodies—either within a single architecture or through tightly aligned components where needed.
Job Responsibility:
Focus on modelling and algorithmic innovation using high‑quality synthetic multi‑view data
Research and develop novel transformer architectures supporting both monocular and multi‑view imagery
Design models that unify face, hand, and body representation within a single framework
Investigate scalable approaches for multi‑person scenarios
Rigorously evaluate proposed methods on both synthetic and real‑world benchmarks
Requirements:
Currently enrolled in a PhD program (senior stage), or a Master’s student with a strong publication record
Peer reviewed publications or preprints in top venues (e.g., ICCV, CVPR, SIGGRAPH, ECCV, NeurIPS, ICLR) or equivalent evidence of research impact
Research experience in computer vision and deep learning, including training, fine tuning, and evaluation of modern vision models
Strong skills in experimental design, quantitative analysis, and clear scientific communication