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Applied Scientist Intern: Differentially Private Video Synthesis

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Microsoft Corporation

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Location:
United Kingdom , Cambridge

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Contract Type:
Not provided

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Salary:

Not provided

Job Description:

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

Additional Information:

Job Posted:
March 01, 2026

Employment Type:
Fulltime
Work Type:
Hybrid work
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