Technical Writing on Chance-constrained Scheduling

Scheduling is a pervasive problem in planning contexts. Imagine situations such as science exploration, airport operations, or any logistics scenario where mistakes and missed deadlines are costly. It is often tricky to schedule because one doesn't know or can't control how long activities will take. Yet we often make plans anyway and hope that the risk of failure is small. *Chance-constrained scheduling* is about quantifying that risk, so that we can more confidently schedule large-scale scenarios, or make existing plans more reliable. 

This posting is for a supervisor-funded position that focuses on the technical writing of the research. You would work closely with the graduate student in charge to develop and communicate state-of-the-art algorithms in chance-constrained scheduling, as well as writing about the background theory. You would also have the chance to collaborate with other students who are focusing on the code. Knowledge of LaTeX and experience with algorithms (6.006) are required. Exposure to artificial intelligence reasoning (6.034, 16.410) is also helpful. This would be a good fit if you are enthusiastic about technical writing, interested in algorithms, and wish to become co-author on a publication.


Faculty supervisor: Brian Williams 
Research group: Model-based Embedded and Robotics Systems
Contact: Andrew Wang