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Research Internships at Microsoft provide a dynamic environment for research careers with a network of world-class research labs led by globally-recognized scientists and engineers, who pursue innovation in a range of scientific and technical disciplines to help solve complex challenges in diverse fields, including computing, healthcare, economics, and the environment. The Networking Research Group (NRG) at Microsoft Research focuses on basic and applied research in networked systems, including network security, verification, sensing, mobile computing and emerging networking challenges in the age of AI. We build systems, engage with academia, publish scientific papers, publish software for the research community, and transfer cutting-edge technologies to Microsoft’s product groups.
Job Responsibility:
Research Interns put inquiry and theory into practice
Learn, collaborate, and network for life
Advance their own careers
Contribute to exciting research and development strides
Collaborate with other Research Interns and researchers
Present findings
Contribute to the vibrant life of the community
Requirements:
Currently enrolled in a Ph.D. program in Computer Science, Electrical Engineering or a related STEM field
Ability to think unconventionally to derive creative and innovative solutions
Have at least one year of experience with systems building
Experience in one of the following: network hardware, physical layer technologies, mobile systems and devices, network architecture, operations, and design, network security and privacy, AI/machine learning, analysis and optimization
Knowledge of large models, and experience training them at scale, or running inference is a plus
Ability to collaborate effectively with your mentor and other researchers
Research Interns are expected to be physically located in their manager’s Microsoft worksite location for the duration of their internship
Submit a minimum of two reference letters for this position as well as a cover letter and any relevant work or research samples
Nice to have:
Knowledge of large models, and experience training them at scale, or running inference is a plus