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Join a strong research team advancing high-fidelity synthetic data generation pipelines. You will work on cutting-edge research in differentially private synthetic data generation, with a particular focus on video generation. Opportunities include advancing our existing work on Private Evolution and Differentially Private Reinforcement Fine-tuning, designing new innovative algorithms for DP synthetic data, and applying them to high-impact real-world projects.
Job Responsibility:
Advance the research: Develop cutting-edge algorithms for differentially private video synthesis, including mechanisms, training objectives, and/or optimization strategies that improve fidelity, stability, and downstream utility
Own experiments end-to-end: Define hypotheses, build rigorous evaluation protocols for synthetic video data (e.g., classification, detection, pose/landmarks, segmentation, tracking), run ablations, and analyze results at scale
Collaborate broadly: Work closely with researchers and engineers to co-design algorithms, benchmarks, and experimental setups aligned with broader program goals
Communicate impact: Present findings clearly, contribute to internal reports, and—where appropriate—author submissions to top-tier research venues
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 image and video synthesis and deep learning, including training, fine tuning, and evaluation of modern vision models
Strong skills in experimental design, quantitative analysis, and clear scientific communication
Nice to have:
Experience with models or agents for video generation
Knowledge of human-centric tasks (pose/landmarks, tracking, activity recognition, face/hand analysis)
Demonstrated ability to collaborate in multidisciplinary teams and to drive projects with minimal supervision