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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. Data systems research at Microsoft Research (MSR) focuses on all aspects of large-scale data systems including query execution, data storage and indexing, resource management for cloud data platforms, data discovery, and data preparation techniques. Candidates with experience in leveraging novel hardware, Artificial Intelligence (AI), and Machine Learning (ML) for data management challenges are of special interest to us. Our past research has been incorporated into Microsoft’s products and services such as SQL Server, Azure SQL Database, Power BI, Excel, and Dynamics 365. We have had influential publications at top conferences such as ACM SIGMOD, VLDB, and IEEE ICDE including several best paper awards and test-of-time awards. More details are available at Data Systems Group at Microsoft Research. During this Research Internship, you will have the opportunity to work with top researchers and engineers at MSR to develop innovative solutions to challenging research problems.
Job Responsibility:
Research Interns put inquiry and theory into practice
learn, collaborate, and network for life
contribute to exciting research and development strides
paired with mentors and expected to collaborate with other Research Interns and researchers, present findings, and contribute to the vibrant life of the community
Requirements:
Currently enrolled in a PhD program in Computer Science or a related STEM field
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
Familiarity with the internals of relational database systems, distributed cache and key-value stores, vector databases, storage engines, model training and inference, and big data analytics systems
Familiarity with query processing and optimization, concurrency, transactions, resource management, scheduling
Familiarity with data cleaning, data transformation, data structure and algorithms
Ability to apply AI and ML techniques to data management problems