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Microsoft Research Intern - Artificial Intelligence Taiwan, Taoyuan City 894951858
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Required Qualifications
- Currently enrolled in a Master or PhD program in Computer Science, Electrical Engineering, Mathematics or a related field.
Other Requirements
- Research Interns are expected to be physically located in their manager’s Microsoft worksite location for the duration of their internship.
- In addition to the qualifications below, you’ll need to submit a minimum of two reference letters for this position as well as a cover letter and any relevant work or research samples. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter.
- Ability to work independently and collaboratively in a dynamic and vibrant research environment.
- Willingness to embrace knowledge outside your field of research interest.
- Solid programming skill in Python, PyTorch and experience conducting experiments and writing papers.
- Familiarity with current various world modeling methods.
- Experience in LLM, either pre-train or post-train.
- Experience in RL, Computer Vision or Signal Processing is a plus.
- Relevant publications such as CVPR, ACL, ICML, ICCV, ECCV, NeurIPS, ICLR, EMNLP etc.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
Additional Responsibilities
- Research and Analysis: Conduct thorough research on world modeling, propose new world modeling framework and further test its validity.
- Documentation and Reporting: Prepare detailed documentation of simulations, methodologies, and findings. Present results and insights to team members and stakeholders.
- Innovation and Problem-Solving: quickly identify the pros/cons of existing world modeling methods and propose novel approaches for world modeling.
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