Research Intern - Machine Learning
 Microsoft | |
 United States, Washington, Redmond | |
 Nov 14, 2024 | |
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OverviewResearch 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.We are a team at Microsoft Research, Redmond focused on developing and applying advanced artificial intelligence (AI) models and algorithms to help solve complex scientific and engineering challenges across the atomistic, process, deployment scales. Our team is diverse, comprising of scientists with expertise across computer science, chemistry/biology, and engineering and is passionate about translating research into real-world impact. We are highly collaborative and draw expertise from teams across Microsoft Research and Azure.
ResponsibilitiesResearch Interns put inquiry and theory into practice. Alongside fellow doctoral candidates and some of the world's best researchers, Research Interns learn, collaborate, and network for life. Research Interns not only advance their own careers, but they also contribute to exciting research and development strides. During the 12-week internship, Research Interns are paired with mentors and expected to collaborate with other Research Interns and researchers, present findings, and contribute to the vibrant life of the community. Research internships are available in all areas of research, and are offered year-round, though they typically begin in the summer. Research Interns will participate in AI projects for process optimization, supply chain modeling, industrial engineering, design and implement advanced models and algorithms, and collaborate with researchers across geographies (if applicable). Research Interns will work with their mentor and group to address an industrial problem with machine learning (ML). Examples could be:Power distribution and grid modelling.Modeling and optimization of industrial processes.Generative design of 3D structures.Multi-agent modeling for decision making. | |