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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. Microsoft Research (MSR) is seeking Research Interns to work at the intersection of artificial intelligence (AI) and process engineering. Research Interns will join ongoing research efforts that explore how AI—especially large language models and agentic AI systems—can support the process of creating, solving, and analyzing chemical process simulations.
Job Responsibility:
Conduct research on applying agentic AI to chemical process simulation
Design and implement experiments, prototypes, or simulations investigating the ability of AI methods to conduct process simulations
Analyze empirical or model‑based results and contribute to technical reports, publications, or internal decision-support tools
Collaborate with mentors and interdisciplinary teams to advance research directions
Present findings and participate in the intellectual life of MSR’s research community
Requirements:
Currently enrolled in a PhD program in Chemical Engineering, Computer Science, Environmental Science, or a related field
One or more publications or substantial research experience in machine learning, computational science, chemical engineering, or related areas
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:
Proficiency in Python or similar programming languages
Experience working with large language models, agentic AI, or AI‑assisted reasoning systems
Demonstrated problem‑solving, communication, and collaboration skills
Prior research contributions demonstrated through papers, prototypes, datasets, or software