Machine Learning for Oligopeptide Design
The advent of modern machine learning (ML) methodology is accelerating scientific progress by streamlining research across disciplines. In chemistry, it enables the modeling of complex structure-function relationships with unprecedented accuracy. Additionally, deep generative models allow for the generation of structures with desired function, thus enabling the design of novel molecules and materials.
Position Details
Position: Postdoctoral Researcher
Start Date: Immediately
The goal of this project is to develop ML models for: 1) predicting properties of oligopeptide materials based on peptide sequence and end-group functionalization; 2) guiding experimental structure optimization workflows to minimize the number of experiments to achieve desired properties; 3) generating new peptide sequences with desired target properties. This postdoc position will be part of the Big Chemistry consortium, which will implement a big data approach to chemical research relying on automated experiments and machine learning. Building on high quality data experimental data from the automated experiments, we will train new AI algorithms to predict the emergent properties of complex molecular systems consisting of oligopeptides. To apply, please submit your application material through the platform linked below.
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