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AI-assisted infrared nanoimaging and spectroscopy

Job Summary

We are currently accepting applications for the above mentioned position. This is a unique opportunity for highly motivated students recently graduated from the University in Physics or related fields to gain research experience in one of DIPC’s high-profile research teams.

Job Description

Scattering-type scanning near-field optical microscopy (s-SNOM) is a technology for infrared (IR) nanoimaging that overcomes the diffraction limit and provides a spatial resolution that is 1000x better than that of conventional IR imaging. This capability makes s-SNOM well suited for the structural and chemical characterization of modern nanomaterials. One particular aspect is that s-SNOM could have an impact on label-free imaging of biological cells and tissue to expand our knowledge of cancer and neurological diseases. On the other hand, artificial intelligence is currently transforming the way we use microscopy techniques, offering to enhance signal quality and accelerate data interpretation. With this project, we seek to advance s-SNOM through the implementation of AI methods. Specifically, we seek to improve signal to noise in s-SNOM by making learn the AI model to distinguish signal from noise from the experimental data itself, so noise can be removed in postprocess. Further, we seek to improve analytical capabilities of s-SNOM through the development of AI-powered s-SNOM models, where we will use high performance computing (HPC) to build s-SNOM models (FDTD or COMSOL) capable to describe real world samples and use AI (e.g. PyTorch) to make these models fast and practical. AI is just beginning to be explored for SNOM, so this is a very good opportunity to join our team of s-SNOM & AI model developers and explore further this exciting intersection of technologies!

We offer a PhD position to develop and explore AI models for s-SNOM. Specifically, we will focus on the following aspects: (i) Become an expert of s-SNOM and get high quality s-SNOM data sets of relevant samples in the biological and polymer sciences sphere (ii) Build and run HPC models of SNOM on the computing infrastructure of the DIPC. (iii) Design and train AI models to improve data quality and enhance the analytical capabilities of s-SNOM Come and join us at the DIPC to pursue your PhD thesis in our international research environment!

Benefits

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