We’re looking to grow!
Interested postdocs should inquire directly at firstname.lastname@example.org with a CV and a brief statement of your research interests and background. Graduate students should apply directly to the MIT Department of Chemical Engineering (note: fee waivers are available through the Dean of Engineering). Undergraduate students looking for research opportunities for pay or credit can also email email@example.com with their interests and any relevant coursework or research experience.
(Postdoctoral Associate) Reaction Informatics and Machine Learning for Synthetic Chemistry. As part of the Machine Learning for Pharmaceutical Discovery and Synthesis consortium, we are looking for a postdoc to develop new algorithms and approaches to modeling organic reactivity. Topics of interest include synthesis planning, reaction prediction, reaction condition prediction, experimental design, and extrapolation to discover new synthetic methods. The ideal candidate will be fluent in Python and have a background in cheminformatics, computational chemistry, or synthetic chemistry. There is an additional opportunity to collaborate on a project related to polymer informatics and design.
(Postdoctoral Associate) Hardware Automation and Control for Autonomous Chemical Synthesis. We are interested in the development of automated hardware platforms capable of testing computationally-proposed hypotheses in a closed-loop manner. The convergence of data science, machine learning, laboratory automation, and chemistry has the potential to revolutionize how molecular synthesis is approached across the pharmaceutical industry and related disciplines. The ideal candidate will have a chemical engineering, chemistry, or mechanical engineering background and experience in laboratory automation; they will be able to develop software for hardware orchestration and scheduling.
(Postdoctoral Associate) Data Analytics and Machine Learning for Biological Manufacturing and Process Development. A new collaboration between MIT and Takeda seeks to accelerate the development of processes for the manufacturing of biologics (e.g., enzymes, monoclonal antibodies). Much effort is spent on empirical screening and data generation to understand the effect of process conditions on process performance. This project will shorten development timelines for biomanufacturing programs by developing quantitative models of how process parameters modulate cell states within continuous cell cultures and how those cell states, in turn, inform overall cell culture performance and product quality. The ideal candidate will have experience building quantitative, data-driven models with relevance to the physical or biological sciences.