We’re looking to grow!
If you might be interested in joining us, please take a look at our group guide for information about our values and my attitude toward research and mentorship.
Interested postdocs should inquire directly at email@example.com 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 firstname.lastname@example.org with their interests and any relevant coursework or research experience.
(Postdoctoral Associate) High-Throughput Polymer Synthesis, Characterization, and Data-Driven Optimization. In collaboration with Professor Brad Olsen at MIT, we have an opening for a new postdoc in the area of machine learning for the development of polymer and biohybrid nanoparticles. The project will involve high-throughput synthesis of polymer libraries and use of automated liquid handling systems to prepare combinatorial libraries of biohybrid particles. These particles will be screened for functional properties, and machine learning methods will be applied to model and predict nanoparticle performance as a function of the primary sequence of polymers. This project is supported by an industrial sponsor and provides a strong opportunity for the postdoc to interact with and learn from members of the entrepreneurial community in Boston as well as the two collaborating MIT groups. A strong candidate will have a Ph.D. in Chemistry, Chemical Engineering, Materials Science, Bioengineering, or a closely related field. The candidate should have demonstrated abilities in polymer synthesis and characterization as well as familiarity with basic molecular and cell biology techniques and a desire to collaborate in the data-driven modeling of polymer systems.
(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 multiple postdocs to develop new algorithms and approaches to modeling organic reactivity. This work will build on prior work in predictive synthesis including the ASKCOS tool for computer-aided synthesis planning. 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 synthetic chemistry, cheminformatics, or computational chemistry.
(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.