- Develop, implement, and evaluate methods for learning robust representations from multi-modal egocentric data (e.g., video, audio, inertial measurement units).
- Make use of Meta’s large infrastructure to scale and speed up experimentation.
- Write modular research code that can be reused in other contexts.
- Collaborate with other researchers.
- Work towards taking on big problems and deliver clear, compelling, and creative solutions to solve them at scale.
- The work should result in publishable research to appear in a top-tier ML or CV conference (e.g., NeurIPS, ICLR, CVPR, ECCV).
- Currently has or is in the process of pursuing a PhD in machine learning, computer vision, speech processing, applied statistics, computational neuroscience, or relevant technical field.
- Excellent research skills involving defining problems, exploring solutions, and analyzing and presenting results.
- Proficiency in python and machine learning libraries (numpy, scikit-learn, scipy, pandas, matplotlib, tensorflow, pytorch).
- Deep understanding of at least one of the following areas: Transfer, few-shot, zero-shot, continual and/or online learning, self-supervised learning, or multi- or cross-modal learning.
- Interpersonal skills: cross-group collaboration and cross-culture collaboration.
- Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment.
- Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICML, ICLR, CHI, UIST, IMWUT, CVPR, ICCV, ECCV, AAAI, ICRA, SIGGRAPH, ETRA, or similar.
- Experience with deep metric learning / neural net embedding methods.
- Experience on vision based input recognition systems, such as hand tracking, body pose estimation.
- Experience on working with time sequence form sensor data, such as IMU and audio.
- Experience working and communicating cross functionally in a team environment.
- Intent to return to degree program after the completion of the internship/co-op.
- Availability for minimum 16 consecutive week internship.
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