PhD2022-05 - AI-assisted rapid detection of pathogens using the innovative approach of nanopore technology

Background

Modern proteomics methods enable large-scale studies of proteins produced in an organism, system, or biological context, and are also becoming increasingly powerful for identifying biological threats, potential disease mechanisms and disease biomarkers. The currently used method for protein identification has some disadvantages. Therefore, the application of nanopore technology for protein sequencing at the single molecule level represents a new innovative approach. Due to the significantly higher complexity of proteins compared to DNA, the complexity of resulting nanopore raw signals also increases. Therefore, adapted ML-based methods are needed to convert these signals into reliable peptide identifications.

Aim

The aim of the project is to develop and optimize machine learning-based signal processing methods for reliable identification of marker peptides using nanopore technology.

AI Methods

You will design a machine learning strategy that learns to process the raw signal into known peptides in real time. Own and publicly available datasets will be used to train the algorithm. You will use Deep Neural Network as well as various supervised learning methods.

Apply via email here: [email protected]

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