The successful candidate will contribute to our development of a system for automating the Design-Make-Test-Analyze cycle in pharmaceutical research.
They will collaborate with synthetic chemists in order to tightly couple the Design and Make phases of the cycle in an automation-friendly way.
Some possible research topics include :
Collaborations with synthetic chemists to optimize reaction conditions and generate the data to build effective AI / ML models for predicting whether a particular set of building blocks will work in a reaction.
Developing and applying active-learning strategies for reaction optimization and compound design.
Developing new systems for capturing, managing, querying, and exploring the experimental data generated during the reaction optimizations computational.
Developing efficient pipelines for defining, enumerating, and screening virtual libraries based on the optimized reactions and available building blocks.
Developing approaches to allow Design tools to be used together with automated synthesis technology.
What does this postdoctoral study offer :
Research at the interfaces of several hot fields : AI assisted drug development, virtual screening and automated synthesis.
Working collaboratively with both synthetic and computational chemists from two research groups and industry.
Applied research project.
Extensive experience with molecular modelling software / Cheminformatics.
Extensive experience with software development.
Solid understanding of organic chemistry and chemical processes.
Experience with modern AI / ML approaches.
Understanding of basic automation techniques, databases, and data analysis tools.