Research Assistant - AI-assisted infrared nanoimaging and spectroscopy (Open)

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!

• The PhD candidate should hold an internationally recognized Master degree (or equivalent) in Physics or a related field. The candidate should have a clear motivation and skill to perform experimental work. Experience in the following areas is welcome but not mandatory:
• Knowledge of optics and nanooptics
• Experience with IR spectroscopy or similar equipment
• Experience with numerical tools (FDTD, Comsol, …)
• Experience with AI tools (e.g. PyTorch, scikit-learn, TensorFlow, …).
• Data processing using Matlab, Python or similar
• Basic knowledge of IR spectroscopy, Nanoimaging (AFM, SNOM,…).
• A good level in written and spoken English is a mandatory prerequisite. You should be able to work independently, are a self-motivated person and able to work in a team.

• Contract duration: 1 year (possibility to extend up to 4 years)
• Estimated annual gross salary: Salary is commensurate with qualifications and consistent with our pay scales
• Target start date: 2025/09/01

We provide a highly stimulating research environment, and unique professional career development opportunities.

We offer and promote a diverse and inclusive environment and welcomes applicants regardless of age, disability, gender, nationality, ethnicity, religion, sexual orientation or gender identity.

The group seeks to develop highly sensitive IR nanospectroscopy technology and apply it to address imaging challenges in the biomedical field. The group also engage in optical modeling of IR nanospectroscopy to provide a framework for better understanding the recorded spectral data and improve image quality, as well as maintain a general interest in the interaction of light with matter molecular vibrations.

Interested candidates, please provide a brief statement of interest (1 page max) where you explain your academic background, your research interests, why you would like to do a PhD at the DIPC, how you think you can contribute and what your long-term career goals are. Please also provide an updated CV and a transcript of records of your Master’s degree. Reference letters are welcome but not indispensable.

• Reference: 2025/39
• Application deadline: 2025/07/31
• Application email: jobs.research@dipc.org

Applications received by the deadline will be evaluated by a Committee designed by the DIPC board on the basis of the following criteria:

• CV of the candidate (60%)
• Adequacy of the candidate’s scientific background to the project (20%)
• Reference letters (10%)
• Other: Diversity in gender, race, nationality, etc. (10%)

Evaluation results will be communicated to the candidates soon after. Positions will only be filled if qualified candidates are found.

The DIPC may revoke its decision if the candidate fails to join by the appointed time, in which case the position will be awarded to the candidate with the next highest score, provided it is above 50 (out of 100).

However, the selected candidate may keep the position if, in the opinion of the Selection Committee, the candidate duly justifies the reasons why he or she cannot join before the specified deadline, and as long as the project allows it.